IMPROVEMENT IN EGG PRODUCTION TRAITS IN THE LIGHT LOCAL CHICKEN ECOTYPE USING A SELECTION INDEX BY OLEFORUH-OKOLEH, VIVIAN UDUMMA (NEE EWA) PG/Ph.D/02/32927 DEPARTMENT OF ANIMAL SCIENCE FACULTY OF AGRICULTURE UNIVERSITY OF NIGERIA, NSUKKA MAY, 2010 IMPROVEMENT IN EGG PRODUCTION TRAITS IN THE LIGHT LOCAL CHICKEN ECOTYPE USING A SELECTION INDEX BY OLEFORUH-OKOLEH, VIVIAN UDUMMA (NEE EWA) PG/Ph.D/02/32927 SUPERVISORS: PROF. DR. C.C. NWOSU PROF. L.N. NWAKALOR DEPARTMENT OF ANIMAL SCIENCE FACULTY OF AGRICULTURE UNIVERSITY OF NIGERIA NSUKKA MAY, 2010 I IMPROVEMENT IN EGG PRODUCTION TRAITS IN THE LIGHT LOCAL CHICKEN ECOTYPE USING A SELECTION INDEX BY OLEFORUH-OKOLEH, VIVIAN UDUMMA (NEE EWA) PG/Ph.D/02/32927 A THESIS SUBMITTED TO THE DEPARTMENT OF ANIMAL SCIENCE, FACULTY OF AGRICULTURE, UNIVERSITY OF NIGERIA, NSUKKA, IN FULFILLMENT FOR THE REQUIREMENT OF THE AWARD OF THE DOCTOR OF PHILOSOPHY DEGREE (Ph.D) IN ANIMAL BREEDING AND GENETICS. SUPERVISORS: PROF. DR. C.C. NWOSU PROF. L.N. NWAKALOR DEPARTMENT OF ANIMAL SCIENCE FACULTY OF AGRICULTURE UNIVERSITY OF NIGERIA NSUKKA MAY, 2010 II CERTIFICATION OLEFORUH-OKOLEH, VIVIAN UDUMMA, a Postgraduate student in the Department of Animal Science, Faculty of Agriculture, University of Nigeria, Nsukka with registration number PG/Ph.D/02/32927 has satisfactorily completed the requirements for the research work for the degree of Doctor of Philosophy in Animal Breeding and Genetics. The work embodied in this Project Report is original and has not been submitted part or full for any other Diploma or Degree of this or any other University, to the best of my knowledge. --------------------------------- ------------------------------------ Professor (Dr) C. C. Nwosu Professor L. N. Nwakalor SUPERVISOR ---------------------------------Dr. S. O. C. UGWU HEAD OF DEPARTMENT SUPERVISOR -----------------------------------EXTERNAL EXAMINER III DEDICATION This work is dedicated to the loving memories of Chief Paul Ewa Aja, Pa Daniel Oko Inya, Hori Daniel Idam Isu, Elizabeth Ugo Isu, Elizabeth Ugo Nwachi and Michael Oko Ezeali. You taught and made me understand that my gender should never deter or limit me from achieving my visions and missions. Your advice gave me the grace and strength to continue and finish this study. IV ACKNOWLEDGEMENT First and foremost, I would like to thank my supervisors Prof (Dr) C.C. Nwosu and Prof. L.N. Nwakalor, for continuous guidance, inspiration, invaluable help, and patience with me throughout the course of this programme. They have sacrificed their time to make my thesis more readable and my arguments intelligible. They also constantly challenged me to expand my point of view as a geneticist/breeder. It is not possible to convey here the depth of the debt of gratitude I owe them. I am grateful to the Department of Animal Science, Ebonyi State University Abakaliki for allowing me to work with her facilities in the Poultry Teaching and Research Unit. I could not have completed all the studies contained in this thesis without the assistance of Profs, M.O. Ozoje, and C. Onwuka of University of Agriculture, Abeokuta; Prof. S.N. Ibe of Michael Okpara University of Agriculture Umudike, and Mr. Femi of EBSUTH, Abakaliki, especially in broadening my knowledge of statistics/genetics data analysis. I do in a most special way appreciate the kindness and assistance of Prof. G. C. Okeke of the Department of Animal Science, University of Nigeria, Nsukka. My parents - Sir and Lady G.A. Ewa, Vincent Agha-Ewah, L., all my siblings (especially Onyinye…I love you), Adeolu Adewale, Uche Muoneke, Felicia Nwanchor and Innocent Omah without your help these project would not have been possible. I have to also appreciate Profs, I.I. Osakwe, Jonny Ogunji, Dr Cosmos Ogbu and my other colleagues (both graduate students at Dept of Animal Science, UNN and staff at EBSU, Abakaliki) for being there when I needed them most. Finally, I most sincerely thank my amiable husband – Barr. Vincent Oleforuh Okoleh: without your support, love, patience and understanding finishing this programme would not have been possible. It was terrible to be apart and I am glad the storm is over. I can only love you more. To you Almighty God – what can I say – thank you for being the author and finisher of my faith! Oleforuh-Okoleh, V.U. (Nee Ewa) University of Nigeria Nsukka May, 2010 V TABLE OF CONTENT Title page … … … … … … … … … … i Certification … … … … … … … … … … ii Dedication … … … … … … … … … iii Acknowledgement … … … … … … … … … iv Table of contents … … … … … … … … … v-vi List of Tables … … … … … … … … … … vii List of Figures … … … … … … … … … … vii Abstract … … … … … … … … … ix INTRODUCTION … … … … … … … … … 1-4 1.1 Background of Study … … … … … … … 1 1.2 Statement of Problem … … … … … … … 2 1.3 Objectives of the Study … … … … … … … 3 1.4 Justification … … CHAPTER ONE … … … … … … … … 3 LITERATURE REVIEW … … … … … … … … 5-16 … … … … … … 5 Factors Influencing egg Production … … … … 5 … … … 5 CHAPTER TWO 2.1 Egg Production in Chicken 2.1.1 2.2 Principles of Selection… … …. 2.3 Selection for Body Weight and Egg Production Traits in Chicken … 9-13 2.4 Use of Selection Index in Chicken 2.5 2.6 … … … … … … 13-14 Basis for Short-Term Egg selection in Poultry … … … 14 The Nigerian Local Chicken … … … … … … 14-16 CHAPTER THREE MATERIALS AND METHOD … … … … … … … 17-25 … … … … … … … 17 3.1 Experimental Site 3.2 Experimental Animals … … … … … … 17 3.3 Foundation Stock … … … … … … … 17 3.4 Management of birds … … … … … … … 18 3.5 Maintenance of a control population … … … … … 20 3.6 Data Collection … … … … … … … 20 3.7 Data Analysis … … … … … … … … 20 Evaluating the Performance Characteristics … … ... 20 3.7.1 VI 3.8 3.7.2 Estimation of Genetic Parameters … … … … 21 3.7.3 Measurement of Selection Applied … … … … 23 Construction of Selection Index using the Selection Criteria … … 24 CHAPTER FOUR RESULTS AND DISCUSSION … … … … … … 27-48 … … … … … … 4.0 Results … … 27-41 4.1 Mean Performance of the Various Egg Production Traits Studied … 27-31 4.1.1 Age at First Egg … … … … … … 27 4.1.2 Body Weight at First Egg … … … … … 27 4.1.3 Weight of First Egg … … … … … … 27 4.1.4 Average Egg Weight … … … … … … 27 4.1.5 Total Egg Number … … … … … 28 Estimates of Genetic Parameters of the Selection Criterion Traits … 32-36 4.2.1 Heritability Estimates … … … 32 Phenotypic and Genetic Correlation between Traits … … 35 … 37-40 4.2 4.2.2 4.3 … … … Measurement of Selection Applied … … … … … … … 4.3.1 Selection Differential, Selection Intensity and Selection Response … 37 4.4 Selection Indexes used for the Selection of LLCE Hens … … 40 4.5 Discussions … … 41-47 … … … … … … 4.5.1 Mean Performance of the Various Egg Production Traits Studied 41 4.5.2 Heritability Estimates … … … 43 4.5.3 Phenotypic and Genetic Correlation between Traits … … 46 4.5.4 Measurement of Selection Applied … … … … … … … 46 CONCLUSION AND RECOMMENDATIONS … … … … … 49-51 REFERENCES … … … … … … … … … 52-65 APPENDICES … … … … … … … … … 66-88 CHAPTER FIVE VII LIST OF TABLES Page Table 1: Table 2: Population Size, Effective Population Size and Change in Inbreeding Coefficient over the Three Generations of Selection … … … Proximate Composition of Commercial Diets … … … 18 19 Table 3: Vaccination Schedule for the Birds … … … … … 19 Table 4: Analysis of variance table … … … … … … 21 Table 5: Analysis of Covariance table … … … … … … 22 Table 6: Mean (±SE) by population for traits studied … … … … 28 Table 7: Mean (± SE) of the Traits Performance by Generation and … … 29 Mean (± SE) performance and phenotypic regression coefficients in selected and control populations … … … … … 30 Heritability (± SE) estimates of the three selection criteria by generation and population of the LLCEa … … … … 34 Genetic (rg), Phenotypic (rp) and Environmental (re) correlation by generation and population of LLCE … …. … …. … 36 Selection differential, Selection Intensity, Expected Direct Response, Estimated Realized Response and Estimated Index Response over three generationsa … … … … … … … 38 Estimated Index Score (Selected Is and Whole Iμ population), Selection Intensity Factor (ῑ), Heritability of Index (h2) Genetic Gain in Aggregate (ΔH) and Correlation of Index and Aggregate Genotype rIH, Expected Annual Genetic Response (ΔGAi) and Generation Interval Li … 41 Population1 Table 8: Table 9: Table 10: Table 11: Table 12: … … … … … … VIII LIST OF FIGURES Page Figure 1: Phenotypic trend of body weight at first egg in 3 generation selection 31 Figure 2: Phenotypic trend of egg weight in 3 generation selection … … 31 Figure 3: Phenotypic trend of egg number in 3 generation selection … … 32 Figure 4: Regression of BWFE response on generation number … … 39 Figure 5: Regression of AEW response on generation number … … … 40 Figure 6: Regression of TEN response on generation number… … … 40 IX ABSTRACT Fifty hens and five cocks from a random mating population of light local chicken ecotype (LLCE) were mated and the fertile eggs hatched to obtain the parent generation (G0) used for this study aimed at improving egg production traits in the LLCE using a selection index. The hens were monitored for short-term (90-days from first day of lay) egg production traits namely: Body Weight at First Egg (BWFE), Average Egg Weight (AEW) and Total Egg Number (TEN). Data obtained were subjected to statistical analysis using SPSS (2001) and paternal half-sib model with Harvey (1990) to estimate descriptive statistics and genetic parameters respectively. These were employed in constructing the selection index. Selection for all the selection criteria (BWFE, AEW and TEN) was in the positive direction. Selected parents were mated to produce next generations – G1 and G2. Selection differentials, selection intensities and genetic response due to selection were also estimated. A control population which spanned for three generations (each generation had its own control population) was used to monitor environmental changes and to estimate the genetic changes due to selection. There were significant increases (P<0.05) in BWFE, AEW, and TEN in the selected populations over the three generations of study such significant increases (P<0.05) were not observed in the control population. Heritability estimates for all traits in all generations and populations were moderate to high. The heritability of the index was also moderate. Such moderate to high heritability estimates indicate high additive genetic variances, implying that these traits were most passed on from the parents to their offspring. Low to high positive genetic and phenotypic correlations was observed between BWFE and AEW in all populations of study. The genetic correlation and phenotypic correlation between BWFE and EN, and between AEW and EN, was moderate to highly negative for all generations and populations of study. A positive genetic correlation was observed between AEW and TEN in G2 of the selected population. A cumulative selection differential of 269.38g, 1.58g and 3.88 eggs were obtained for BWFE, AEW and TEN respectively. Selection response for traits increased over the generations in a fairly linear manner. Realized response per generation was estimated to be 94.22g, 0.84g and 4.85eggs for BWFE, AEW and TEN respectively. It is evident that the simultaneous inclusion of BWFE, AEW, and TEN in a selection index generally improved the performance of selected birds over the generations in the Light Local Chicken Ecotype. 1 CHAPTER ONE INTRODUCTION 1.1 BACKGROUND OF STUDY The report of the 4th Food and Agriculture Organization (1973) expert consultation on Animal Genetic Resources presented two important objections concerning the endeavour to improve and conserve the local chicken. The first was as to whether the local strains still possess genes, which are useful to the vastly improved exotic strains given the centuries of genetic screening, which the latter have undergone. The second objection pointed to the fact that the process of further screening of the local chicken will be long, laborious and very expensive. However, the results of recent research using local chicken (Ikeobi and Peters, 1996; Ayorinde et al., 2001; Udeh and Omeje, 2001) indicate that the local chicken is a repository of advantageous genes. Secondly, with molecular genetic techniques, genetic improvement of the local chicken is fast and less expensive. Incidentally, third world countries, which established breeding programmes based on the dilution of indigenous germplasm by extensive crossbreeding programme, suffered failures. Those failed efforts have made livestock breeders aware of the importance of indigenous breeds in overall food production systems – because of their adaptation to the environmental stress of the tropics. In spite of the large number of livestock and poultry in the nation, the animal protein intake per caput per day still falls below the minimum requirement level recommended by UN/FAO (Ayodele and Ajani, 1999). This has been traced to the low production of animals, which could be due to genetic and/or environmental constraints. The above underscores the need to improve the level of animal protein production in Nigeria. Of greater importance is the improvement of the poultry sector since it has a number of advantages – including short generation interval, and production of large number of offspring, due to its peculiar reproductive traits (Ibe, 2001). Furthermore, poultry meat is generally accepted by all religions and societies. In many countries the development of agriculture and breeding programmes has resulted in serious changes in poultry breeding stocks during the last decades. The establishment of breeding institutions has led to a pronounced supra-regional propagation of certain chicken breeds due to improvements in performance. As a consequence the local breeds have decreased continuously to the same extent as the preferred high performance breeds have expanded. For instance, it was in a bid to satisfy the need for increased production and profitability in intensive production systems to meet the increase in demand for animal protein by the populace that new high yielding and fast growing poultry breeds 2 were introduced into the existing poultry production systems in Nigeria since the late 1950’s (Obioha, 1992). Incidentally, such introduction has resulted in non-integration of the local breeds considered as ‘low producers’ into large-scale poultry production. Nigeria has rich chicken genetic resources. A good number of workers have documented the characteristics of the local chicken, in terms of morphological, physiological, behavioral and production attributes. Nwosu (1990) gave a review of these. Ibe (1990a, b) identified some major genes of tropical relevance in Nigerian local chicken populations. Perhaps, the most distinguishing feature for physical characterization at present is the body weight of the local chicken found in the various ecological zones of the country (Olori and Soniaya 1992). Observations have shown that local chicken found within the swampy rainforest and guinea savanna regions are lighter in weight than those found within the highlands and sudan savanna regions (Nwosu, 1979). Such differences in body weight can be used to categorize the local chicken broadly as Light Ecotype – those with lighter weight and Heavy Ecotype – those with heavier weight. Research data on the local chicken in the past 50 years (Hill, 1954; Oluyemi, 1979; Omeje and Nwosu, 1982; Nwosu, 1987; Udeh and Omeje, 2001) indicate that the Nigerian local chicken has useful genetic attributes that can be harnessed in crossbreeding programmes for the development of egg-type and meat-type chickens. However, there exist limitations to the realization of total heterosis in such crosses with the exotic because - the local chicken is unpedigreed, unselected and unsegregated (Omeje, 1985) hence, unlike the exotics the local chicken cannot be considered a purebred. Furthermore, crossbreds from purebred parents show heterosis to the extent that their gene frequencies differ unlike hybrids from similar lines that manifest total heterosis, (Pirchner, 1983). In order to incorporate the local chicken as a parent breed to produce strains of chicken that are adaptable to the local environment as well as achieving the much desired goal of making Nigeria self-sufficient in the sourcing of poultry breeding stock and boosting her poultry industry, there is need for selective breeding. The practice of selective breeding among local strains has been found advantageous (El-Issawi, 1975). The concept underlying selective breeding is variation. For within a group of individuals there exist allelic variations that affect the outcomes of quantitative traits such as growth, egg production and egg quality traits. 1.2 STATEMENT OF PROBLEM Not much study has been done on selection of local chicken for meat or egg production; most of the studies have been on crossbreeding with exotic birds. Oluyemi 3 (1979) after seven generations of mass selection on 12-week body weight of the local chicken concluded that the local chicken is not a potential broiler strain. Although the local chicken has been termed a low producer with regards to egg production (40-80 eggs /bird/year under extensive management system), studies relating to the development of the local chicken as a potential layer have shown appreciable improvement in egg production traits of the birds under improved management system (Hill and Modebe, 1961; Nwosu et al, 1979, Omeje, 1985; Tule, 2005). Nwosu and Omeje (1985) further noted that the local chicken has a genetic potential of producing 128 eggs /bird/year. It should be noted that the results of these studies were from random-bred populations. It is quite possible that the local chicken subjected to selection and improved management can do better. This has prompted the present selection study of the local chicken using a selection index approach. 1.3 OBJECTIVES OF STUDY The objectives of this study are to: 1. Estimate genetic parameters, namely heritability of body weight at first egg, egg number and egg weight, and genetic and phenotypic correlations between these traits in the Light Local Chicken Ecotype (LLCE); 2. Develop an appropriate selection index (I) for the selection of LLCE using body weight at first egg, and short-term (90 days from first egg) egg production and egg weight; 3. Evaluate and summarize selection applied and response over three generations of selection. 1.4 JUSTIFICATION Over the last decades, poultry management techniques in Nigeria have improved significantly, with rapidly increasing production. However, due to the high cost of production inputs: such as feed and drugs, and the control of the market by the few livestock contract companies, many individuals and farmers could not compete with the companies and had to give up chicken meat/egg production. For these individual farmers, a streamlined production of local chicken could be an option for alternative income generation and for diversification of the agricultural production base. Akinwumi (1979) reported that 92% of poultry production in Nigeria was derived from indigenous poultry stock. Similar reports though from Asian survey carried out by Prawirokusmo (1988) stated that about 40% of the egg production and 30% meat production 4 in Indonesia was a contribution made by the local type of chicken. Local chickens may appear to produce less than highly specialized exotic breeds, but they are highly productive in their use of local resources and more sustainable over the long term. Products from local poultry stocks are widely preferred because of pigmentation, taste and leanness (Haitook et al, 2003; Horst, 1988). Local chicken can thrive with limited care and feeding and are often more tolerant or resistant to diseases. They are also better able to cope with drastic changes in food and water supplies as well as harsh, variable and extreme weather and climatic condition. By neglecting to develop locally adapted breeds for higher productivity, an opportunity is being missed to help the developing world feed its people. Barker (1982) argued that there are large phenotypic and possibly genetic variations existing within the indigenous/local breeds and varieties. He suggested that the application of genetics towards improving these stocks should be undertaken through proper evaluation and documentation of these breeds on a suitable selection procedure designed to provide an optimum genotype to the farmer. This implies that a breeding strategy, which recognizes the introduction and development of pure breeds and selection within local breeds, is beneficial. Ahmed and Hasnath (1983) described the usefulness of such strategy in native Delish chicken. Hence, continuous efforts to develop pure lines for meat and egg production locally may equal or excel in the future the best currently available in the country. No detailed examination of genetic parameter estimates/variance components for egg production traits in Nigerian local chicken have been reported in literature. Consequently, the little breeding experimental programmes on this bird rely heavily on estimates obtained from exotic populations. For effective genetic improvement, knowledge of genetic parameter estimates of the particular breed or population to be improved is essential. Thus this study is imperative to achieving the genetic improvement of the Nigerian light local chicken ecotype. 5 CHAPTER TWO LITERATURE REVIEW 2.1 EGG PRODUCTION IN CHICKENS The egg production of a chicken is a result of many genes acting on a large number of biochemical processes, which in turn control a range of anatomical and physiological traits. With appropriate environmental conditions (nutrition, light, ambient temperature, water, sound health, etc.), the many genes controlling all the processes associated with egg production can act to allow the chicken to express fully its genetic potentials (Fairfull and Gowe, 1990). 2.1.1 SOME FACTORS INFLUENCING EGG PRODUCTION 1. BODY WEIGHT Body weight is regarded as a function of framework or size of the animal and its condition (Phillip, 1970). One of the main factors influencing egg size is body size (Robinson and Sheridan, 1982). Variation in body weight within a flock can be attributed to genetic variation and environmental factors that impinge on individuals (Ayorinde and Oke, 1995). The poultry producer wants birds of minimum possible size and weights that will maximize production of standard sized eggs at an economic rate and still maintain market carcass value at the end of the production period (Oke, et al. 2004). Body weight in poultry is known to be moderate to highly heritable and hence the selection of heavier individuals in a population of Nigerian light local chicken ecotype for example, should result in genetic improvement of the trait. Though various factors are known to affect egg production, there are conflicting reports on the effect of body weight on egg production. Evidence obtained by Du Plessi and Erasmus (1972) indicated that larger hens within a bloodline laid larger eggs than those with smaller body weights. Telloni et al. (1973) found that hens carrying the dwarf allele (dw) laid fewer eggs than the normal hens (dwB), even when hens of the same body size were compared. However, Quemenur et al. (1988), cited by Fairfull and Gowe (1990), reported that dwarf broiler females lay as well as normal broiler females. Various findings using broiler birds in egg production experiment tend to suggest the possibility that the lower egg production of dwarf chickens may be due to close linkage of the dwarfing gene and genes determining egg production on the sex chromosome. For the development of egg production strains within the local chicken, it is necessary to establish the nature of the relationship existing between body weights of the local chicken and egg production parameters. 6 2. AGE The age at which a hen begins to lay eggs affects the total egg production in its life cycle, all things being equal. Selection of laying hens is normally based on partial records; improvement in production occurs largely in the first part of the laying cycle. Khalil et al. (2004) found that selection of hens with lower age at first egg leads to improvement of performance of egg production. Nwagu et al. (2007b) obtained a positive response in a female line population which they attributed to reduced age at sexual maturity. Liljedahl and Weyde (1980) had reported that contribution of age at sexual maturity to response to selection lies between 50 and 80% over 4 generations of selection. Age influences egg production especially within the first laying cycle and over the subsequent laying cycles (Gowe and Fairfull, 1982) in each laying cycle, egg production (per hen housed or per live hen) quickly rises to a peak and declines slowly thereafter to the end of the cycle, usually terminating with a natural or induced molt. In successive cycles of egg production, the peak egg production of a flock is usually lower and the rate of decline of egg production is more rapid (Fairfull, 1982). In other words most traits deteriorate with advancing age. The decline in weekly egg production throughout the cycle is well studied (Gavora et al., 1982; McMillian et al., 1986; Yang et al. 1989). Thus, within egg production cycles, egg production declines with increasing age while its variation increases. 3. DISEASE Disease affects egg production through mortality and morbidity (sub-clinical and clinical disease, inadequate nutrition, toxic elements, etc.). Mortality reduces the number of layers available to lay eggs, and morbidity reduces the laying ability of affected hens (Fairfull and Gowe, 1990). However the effects of morbidity and mortality on egg production records depend upon the age of the hens when affected. For instance, if birds are affected towards the end of the production period, little is lost in terms of economic returns in egg production, however, infection or conditions of instability in hens before or about their peak period of egg production would greatly affect the overall egg production records. 2.2 PRINCIPLES OF SELECTION The purpose of applied poultry breeding is to improve production qualities of the domestic fowl. Although altering and improving the environment, or physiological situation or manipulation of the animals contribute immensely towards improvement of their production qualities, the possibility remains that variation nevertheless still exists after 7 optimum non-hereditary conditions have been established. This is because some of the variations in the economic traits are genetic in character and improvement brought about by heredity tends to be permanent. The ultimate goal of a breeding programme is genetic improvement of traits defined in the breeding objective for the animal population. The poultry breeder does this by ranking his animals, culling those with the poorest evaluation while selecting the best evaluations as replacements. With successful selection, the progeny generation will on the average be better than the average of the population from which the parents were chosen, resulting in a genetic progress being obtained. The principle of selection, thus, is an integral concept in animal breeding – it is the basis of genetic improvement programme (Cameron, 1997). Selection means differences in reproductive rates in a population, whereby animals with some characteristics tend to have more offspring than animals without these characteristics. That is, selection refers to the practice of causing or permitting superior individuals to produce more offspring than inferior ones. More precisely, selection is essentially concerned with replacing an existing population with one that is genetically superior. It can be defined as choosing of animals of higher genetic merit than average, to be parents of the next generation. Thus, the genes of the favoured animals tend to become more abundant in the population and those of the less favoured animals less abundant (Lerner, 1950). So far as genes produce effects which are consistently desirable in all combinations with other genes, the changes produced by selection are permanent (unless and until equally effective counter-selection has taken place); but in so far as the effects of the genes are desirable in some combinations and undesirable in others, many of the changes which selection produces when it is first practiced are lost (Lush, 1965). Genetic improvement may, therefore, consequently be measured by the change in a population mean or gene frequency from generation to generation (Leymaster et al., 1979). Invariably, selection can be performed both between and within populations (e.g. breeds). To screen animal populations and thereafter use those that have characteristics in line with a desired breeding goal can be a way to get results quickly, assuming the populations can be properly compared. For continuous and long-lasting effects, it is necessary to conduct selection within populations (Strandberg and Malmfors, 2006). The genetic progress, improvement, or change that can be attained in a trait/ several traits is influenced by the following factors. These factors include the following – heritability, selection differentials, generation interval, and genetic, phenotypic, and environmental correlations between traits. The driving force for genetic improvement is 8 heritability defined by the genetic superiority achieved by the selection of parents. Not only does it provide the breeder with a measure of genetic variation, and of the variations upon which all the possibilities of changing population by breeding methods depend, but also, its importance rests on its properties as a measurement of the accuracy with which a genotype can be identified from the phenotype of an individual or of a group of individuals (Adedeji et al., 2006). Heritability shows how important efforts to improve a trait through improved management or environmental conditions may be compared to genetic selection (Akbas et al., 2002). Although heritability estimation allows the prediction of the amount of gain expected from given amount of selection, the accuracy of heritability estimate is valid only for the particular generation of the specific population from which the data used in arriving at it was derived. The methods that have been most commonly used to estimate the heritability of various traits observed in chickens are: parental half-sib correlation, maternal half-sib correlation, full-sib correlation, parent-offspring regression, and realized heritability (Kinney, 1969). Heritability estimation from the full-sib correlation may be biased as the maternal effect; the common environmental effect and the dominance effect are included in the dam variance component (Falconer, 1981). Higher heritability estimates for dam component than those of sire component for traits show the existence of dominance deviations and/or maternal effects (Oni et al., 2000). The changes in allelic frequency under selection act as a fraction of the selective advantage of the desired genotype and of the reproductive potential of the population. Selection differential is the increment between the mean of the selected group and the population from which they were selected (Nordskog, 1981). The magnitude of the selection differential depends on two factors namely: the proportion of the population included among the selected group, and the phenotypic standard deviation of the character. As the proportion of animals selected to be parent’s decreases, the mean predicted genetic merit of selected animals’ increases (Al-Murrani, 1974). Since it is the intention of the breeder to make as much genetic gain as possible, it is of fundamental importance to make selection differential as large as possible. Cumulative selection differential is often used to quantify total selection pressure applied (Frahm et al., 1985). The cumulative selection differential can be compared with total response to evaluate effectiveness of selection for the primary trait. Lush (1965) expressed selection intensity as the percentage of the population permitted to reproduce itself and is used to express the amount of selection applied in a breeding population. Reduced selection intensities invariably yield decreased selection 9 response per generation (Eisen et al., 1973). Selection response also referred to as the genetic gain and measured as fundamental improvement/year, is the most critical aspect of efficiency of a breeding plan (Falconer and Mackay, 1996). The expected genetic gain, ΔG or response to selection, R is the difference between the mean phenotypes of the progeny and parental generations, which can be predicted given the selection differential, ΔS, and the regression coefficient relating genotype to phenotype (Gjedrem and Thodesen, 2005). Genetic gain depends on: how well the animals are evaluated or the accuracy of prediction; the amount of selection or selection intensity; the magnitude of genetic differences among animals or standard deviation of additive genetic values and; how rapidly better younger animals replace their parents, known as generation interval. The generation interval is defined as the average age of parents when their offspring are born, where the offspring are parents of the next generation (Ibe, 1998). The length of the generation interval must be considered when evaluating alternative selection strategies (Cameron, 1997). Barria and Bradford (1981) noted that a breeder aims at reducing the generation interval at the same time increasing selection response. Chambers (1990) indicated that sexual maturity had much influence on egg production whereby early maturing birds have more opportunities to lay than those which mature later. Consequently, most breeders base a large part of their selection on part records of egg production. Lerner (1958) asserted that the reduction in generation interval made possible by selection of parents at approximately 40 weeks of age outweighs the added precision of a full year’s record in attaining the goal of improved egg production over the first laying year. Hill et al. (1996) noted that breeding efficiency could be lowered seriously by postponing the first breeding to a needlessly late age Genetic relationships occur between various traits in chicken due to gene actions such as linkage and pleiotropy (Zhao et al., 2007). Genetic correlations have always been an important part of carefully constructed breeding programs (Cassell, 2001). Consequently, much more is known today about genetic correlations between economically important traits in different environments than what was known when only the animal model was used in genetic experiments. 10 2.3 SELECTION FOR BODY WEIGHT AND EGG PRODUCTION TRAITS IN CHICKEN The selection scheme used to change an animal’s pattern of growth results in both short-term and long-term effects on other traits such as tissue growth patterns, the onset of sexual maturity, and overall reproductive efficiency. Genetic selection in various poultry species has resulted in increased body weight at various points along the growth curve (Anthony et al., 1991). Selection for body weight in poultry species and the resulting effects on egg production traits has been addressed in numerous studies (Dunnington and Siegel, 1984, Marks, 1987; Siegel and Dunnington, 1987). The common conclusion suggests that the onset of sexual maturity results from the interaction of body composition, age, and body weight. Soller et al. (1984) investigated the minimum weight for onset of sexual maturity in chickens. They estimated the heritability of minimum weight at sexual maturity and reported it to be .34 and .84 for the chicken populations they studied, in which minimum weight at sexual maturity was closely correlated to early growth rate. Data from Oruwari and Brody (1988) concluded that an interaction between chronological age, body weight, and body composition at the onset of sexual maturity are inseparable. Zelenka et al. (1984) suggests that multiple thresholds of minimum chronological age, body weight and body composition result in females reaching sexual maturity. Danbaro et al. (1995) noted that heritability estimates for body weight at 7 weeks and 30 weeks were between 0.10 and 0.34 for all lines used in their study of genetic parameter estimates from a selection experiment in broiler breeders. Chambers (1990) reported that heritability based on additive genetic effects were about 0.4 for growth traits in chicken. Besbes et al. (1992) reported estimates of heritability for 40-week body weight of egg-laying type chicken using Restricted Maximum Likelihood with animal model as 0.5. This is close to the range of 0.59 obtained by Wei and Van der Werf (1992). Hagger (1993) reported estimates of 0.732 and 0.790 for body weight in females and males respectively in a layer strain while Jorjani et al. (1993) obtained a range of 0.54 to 0.65 estimates for body weight. Momoh and Nwosu (2009) worked with a Nigerian heavy chicken ecotype and reported that values of heritability estimates of body weight of the heavy ecotype increased from 0.18 at 4 weeks to 0.43 at 8 weeks and thereafter declined to 0.16 at the 16th week to rise again to 0.30 at the 20th week. They concluded that on the average, the body weight of the heavy ecotype could be described as being lowly to moderately heritable and suggested that the heavy ecotype has dual potential to be selected either as a meat- type or egg- type bird. 11 Estimates of genetic parameters for egg production traits have been extensively reported in egg-type chickens. Kinney (1969) obtained an average heritability for early egg weight in light breeds – using white leghorn, its classical and reciprocal with Rhode Island Red – as 0.45, 0.53, 0.45 and 0.52. These were obtained respectively from sire, dam, and sire and dam covariance components, and daughter-dam regression and indicated that in light breeds, egg weight had high heritability. Oni et al. (2000) reported that estimates of heritability pooled over generations from sire, dam and sire and dam components of variance were 0.15, 0.2, and 0.18 for age at sexual maturity; 0.13, 0.16 and 0.15 for egg number for 280 days of age; 0.24, 0.2 and 0.24 for average egg weight and 0.07, 0.18 and 0.16 for 40week body weight for a strain of Rhode Island chicken under selection. Edriss et al. (1999) cited by Vali (2008) examined heritability coefficient of laying characteristics of indigenous chickens of Iran using sire variance component and reported estimates of 0.26, 0.86, and 0.80 for age at sexual maturity, number of eggs at 34 weeks of age and egg weight from maturity age to 34 weeks respectively. Nordskog (1981) reported the following heritability estimates of egg production traits by age and breed of chickens: average short term rate of egg production, 0.8; sexual maturity, 0.38 (Light breeds); early egg weight, 0.45 (Light breeds); early egg weight, 0.57 (Heavy breeds); mature egg weight, 0.46 (Light breeds); mature egg weight, 0.58 (heavy breeds); fertility, 0.02; and hatchability, 0.19. The reported estimates of heritability for egg number varied from 0.11 to 0.53 (Francesh et al., 1997; Nurgiartiningsih et al., 2002, 2004; Szwaczkowski, 2003). Luo et al (2007) reported heritability estimates of cumulative egg numbers within the range of 0.16 to 0.54 in their study using broiler breeder strains and suggested that the result indicates a moderate to low additive genetic variance for egg production in broiler breeders. They further noted that the estimates of heritabilities were relatively low at the beginning of the laying period and attributed this to the significant physiological changes for hens commencing egg production. Soltan (1997) obtained a significant selection response or genetic progress for egg number (ninety-days from first lay) in Baladi fowl of 7.1 eggs in three generations of mass selection. Venkatramaiah et al. (1986) reported an average genetic change per generation of 2.16 eggs in egg number and 146g of egg mass in selected sublines of White Leghorn. Many studies have demonstrated that reproductive performance of hens decreases as birds become heavier and fatter – this especially evident for broiler breeders (Applely et al., 1994; Chen et al., 2006). It has been shown that when selecting for traits besides reproductive fitness; the result is a negative correlation between the selected trait and reproductive fitness (Lerner, 1954; 12 Nestor, 1977; Falconer, 1981; Dunnington and Siegel, 1984; Marks, 1987). This negative correlation has shown to be true between growth rate and reproductive traits such as female fertility and egg traits (Goodman and Shealey, 1977). With female reproduction, there are some positive correlations between growth and reproduction but majority are reported to be negative. For example, there is a positive correlation between body and egg weight, yet a negative relationship has been shown between high pullet body weight and normal egg production (Siegel, 1963). There are factors such as breed, management, and nutrition that influence the onset of sexual maturity. Long-term selection for growth in chickens has also resulted in the decrease in reproductive traits (Siegel and Dunnington 1985). Data from Japanese quail selected for heavier body weight under different nutritional environments has shown decreases in hatchability, egg production, and increased abdominal and carcass fat (Marks 1991). Genetic correlation between different part records of egg production is an important parameter for describing the dynamics of egg production and designing an early selection program. Besbes et al. (1992) reported that the genetic correlation between egg production for 26 to 38 wk and 26 to 54 wk was 0.66. Luo et al. (2007) worked with a broiler breed and indicated that the genetic correlations between the cumulative eggs for production week 19 and the total cumulative eggs till week 40 were as high as 0.81, and the genetic correlation between the fifth monthly records (egg production from production week 16 to 20) and the total cumulative egg numbers was 0.95. They concluded that in a balanced consideration of selection response and generation interval, early selection based on the first 19 week of cumulative egg numbers could effectively improve annual egg production in the broiler dam line. Harms et al. (1982) reported a negative correlation between body weight and egg number and that egg weight increased with body weight in a linear fashion from onset of egg production. Everett and Olusanya (1985) indicated that larger hens within a bloodline laid larger eggs than those with smaller body weight. This is supported by Ricklefs (1983), who reported that large body size resulted in large egg length, width, and mass, all factors affecting egg weight. Akbas et al., (2002) showed that there is a medium to high negative correlation between age at first egg and egg number (for the first twelve weeks of lay) and noted that this could indicate that cost of production could be lowered by lowering this trait and increasing egg production without significant diverse effect on body weight and egg weight. Adedeji et al. (2006) obtained a negative and low genetic and phenotypic correlation 13 between egg number and egg weight. Similar report was given by Sabri et al. (1999) and Zieba and Lnkaszewicz (2003). Under subtropical environments, growth rate as well as egg production are generally depressed by high ambient temperature (Bordas and Merat, 1984; Deeb and Cahaner, 2001). A significant interaction between environments and lines was found for laying traits, which indicated that the selected line was more sensitive to environmental changes than the control line for production level (Chen et al., 2004). Chen et al. (2009) studied the performance comparison of dwarf laying hens segregating for the naked neck gene in two different environments – in France and Taiwan. In the selected line, the estimated heritabilities exhibited higher values for performance recorded in France than in Taiwan. These they attributed to the large environmental variability in Taiwan and concluded that changes in environment affected genetic parameters to a larger extent. This affirms the premise that estimating genetic correlations between traits measured in two environments is one approach to reveal within-line genotype × environment (G × E) interactions (Mathur and Horst, 1994). 2.4 USE OF SELECTION INDEX IN CHICKEN The theory and methods of deriving selection indices have been adequately developed in the literature (Smith, 1937; Hazel, 1943; Robinson et al., 1951). Hazel and Lush (1942) established the utility of a linear function of traits as a basis for multiple trait selection. In their work, they described the fundamental principles of index construction. They demonstrated how selection indices could be arrived at using nexus of the characters (with characters correlated to each other). Selection index theory, thus, provides an objective method of selecting a linear function of several traits by maximizing a combination of all relevant information (including family and individual phenotypic values, genetic and phenotypic variances and covariance with relative economic weights) into a single value (I) that is ranked for selection (Nwagu et al., 2007b). The fundamentals in the understanding these relationships is based on Sewall Wright’s (1934) path coefficient model. The use of a selection index can increase the efficiency of simultaneous improvement in multiple egg production traits more than when selection is directed towards a single trait. Johari et al. (1999) noted that index selection has been a very effective method for improving egg production in chicken. Sharma et al. (1983) showed that selections on the basis of an index (using data on body weight at 8 weeks, egg production to 300 days, and percent hatchability) was relatively more efficient than tandem selection or independent culling levels selection for all traits except hatchability. Comparisons between index selection and mass 14 selection for various traits such as body weight at 8 and 20 weeks of age, 35-week egg weight, age at sexual maturity, and egg production to 260 days of age in white leghorn strains were made by Verma et al. (1984). For aggregate genetic response, index selection was found to be 2.76, 3.33, 13.66, 1.32 and 1.53 times more efficient than direct selection for egg production, egg weight at 35-week of age, initial egg weight, 20-week body weight and 8week body weight respectively. Singh et al. (1984) compared various selection indices to improve egg production in white leghorn flocks when 10% of the cockerels were selected in each population, selection of 40% of the females based on index selection was found to give better genetic response than when selection with different intensity was made on individual traits. Ayyagari et al. (1985) recorded similar observations in another white leghorn population and so did Makarechian et al. (1983) while working with indigenous chickens of southern Iran. 2.5 BASIS FOR SHORT TERM EGG SELECTION IN POULTRY Selection for early period part-records, generally up to 40 wk of age, is the usual approach for improving egg production in egg-type and meat-type chickens (Dempster and Lerner, 1947; Fairfull and Gowe, 1990). The first study on using partial egg production data as a selection criterion was according to Dickerson and Hazel (1944). In that study, they had reported that improvement was maximized when the interval between generations was kept in a minimum (Lowe and Garwood, 1980). Kinney (1969) observed that short-term records of egg production have positive correlation to full-time. Lowe and Garwood (1980) concluded that in poultry, selection on the early part of the egg record and improvement in animal production is a standard practice in poultry breeding. Bohren (1970) supported this concept by empirically showing that annual egg production is estimated with reasonable precision from an earlier part of the egg record. Nestor et al. (1996) and John et al. (2000) reported an average of 0.45 as genetic correlation between partial and residual egg number from Light Sussex and brown leghorn population. There are many advantages of selection based on partial records genetic advantage; these include to shorten the generation interval; reduce record keeping; achieve higher reproductive rates from layer; utilize high genetic correlation between part-time and full-time performance; achieve rapid selection response (Foster, 1981; Boukila et al., 1987; Nwosu, 1990). However, selection based on part-records has significant unfavorable effects on some important traits, including earlier age at first egg, poorer laying persistency after peak (Yang, 1994), and poor selection accuracy (Luo et al., 2007). 15 2.6 THE NIGERIAN LOCAL CHICKEN Nigeria is richly endowed with a large population of local chickens. According to RIM (1992) the local chicken population in Nigeria is about 103 million, 85% of which are found in the north and the rest in the southern part of the country. Local chickens are kept for various purposes, which include the provision of meat and eggs for home consumption, religious ceremonies and barter, as such they play a big role in rural as well as national economy (FAO, 2004). What is generally referred to as local chicken is a pool of heterogeneous individuals, which differ, in adult size, body weight, feather and plumage pattern (Fayeye et al., 2005). Several authors have reported on the unique adaptation features of the Nigerian local chicken (Adebambo et al., 1999; Ibe, 1993). These include their relatively small adult size, generally flighty nature, relatively thick eggshell, high tolerance to some tropical diseases and parasites and the presence of some major genes affecting feather structure and feather distribution for example naked neck and frizzle feathers. Peters et al. (2004) noted that the major genes of frizzling and naked neck are important as they enhance the themo-regulatory activities of the birds. There have been various genetic studies on the Nigerian local chicken round the ecological zones of the country – in the east by Nwosu and Omeje (1985), Udeh and Omeje (2001), Tule (2005) and Momoh et al. (2007), in the west by Adebambo et al. (1999) and Peters et al. (2004) and in the north by Fayeye et al. (2005). Their main aims were mainly to characterize the Nigerian local chicken using its physical features and crossbreeding to monitor the performance of the progenies. Two major strategies have been offered to achieve new breed development: intense selection within the population followed by crossing (Osman and Robertson, 1968), and crossbreeding to create a heterogeneous broad-based population followed by intense selection (Pirchner, 1983). The first strategy requires extensive sampling of local chickens throughout the country to select desirable genetic material that will constitute the base population, which will subsequently be crossed either with themselves or with other populations. Success of this strategy will largely depend on the initial response to selection. Oluyemi (1979) did not obtain appreciable response following 7 years of selection for 12-week body weight within the Nigerian local chicken population. This is despite the belief that considerable additive genetic variation exists in the local chicken population since they have not been subjected to any conscious artificial selection (Ibe, 2001). The findings of Oluyemi (1979) tend to suggest that the Nigerian local chicken would not be suitable for development as a broiler breed. Incidentally, several works by researchers on the Nigerian 16 local chicken have shown that breeding and developing this chicken into a layer stock would be more efficient and highly achievable. For instance, Nwosu (1990) reported that the local chicken have potential for egg production by citing Nwosu and Omeje (1985) who reported an annual egg number of 146 eggs for the local chicken under battery cage system of management. This was better than the 80-112 eggs per hen per year reported by Sonaiya et al. (1998) for the Nigerian local chicken, although this was the local chicken under semiintensive system of management. The work of Momoh et al. (2007) also affirms that of Nwosu and Omeje (1985). They reported a total egg number of about 144 and 136 eggs respectively for the light and heavy ecotypes of the Nigerian local chicken. 17 CHAPTER THREE MATERIALS AND METHODS 3.1 EXPERIMENTAL SITE The study was carried out at the Teaching and Research Farm of the Department of Animal Production and Fisheries Management, Ebonyi State University, Abakaliki, Ebonyi State, Nigeria. Abakaliki is located between Latitude 06o 4’N and Longitude 08o 65’E in the derived savanna ecological zone of Nigeria. Naturally, the day length of Abakaliki ranges between 12-14 hours all year round. Abakaliki has an annual mean rainfall range of between 1500-2250mm with mean daily temperature ranges of 27oC and relative humidity of 85% (Nwakpu, 2005). The experiment lasted for four years from 2003 to 2007 in which data on egg production traits were collected. 3.2 EXPERIMENTAL ANIMALS The base population for the selection experiment was obtained from random-bred local chicken population maintained at the poultry research unit of the Department of Animal Science, University of Nigeria, Nsukka farm. The birds, which for the purpose of this study have been classified as Light Local Chicken Ecotypes, were obtained from the swampy, rainforest and derived guinea savanna ecological zones of southeastern Nigeria. The areas are Pie-Zamara and Yenegoa in Bayelsa State, Abakaliki in Ebonyi state, Nsukka in Enugu State, Ndoro in Ikwuano of Abia State and Obo-Anag in Ikot-Ekpene, Akwa-Ibom State. These birds were sourced from these areas during the National Coordinated Research Projects (NCRP) and brought to the research Farm of the Department of Animal Science, University of Nigeria, Nsukka. This light local chicken ecotype generally has an average mature body weight ranging from 0.86 to 1.5kg (Tule, 2005). 3.3 FOUNDATION/BREEDING STOCK Five (5) breeding males and fifty (50) breeding females of the light local chicken ecotype parents were used as foundation stock for the selection experiment. In a mating ratio of one cock to ten hens, the foundation population was replicated in five breeding groups. The birds were fed commercial layers diet (CP 17%) daily on an ad libitum feeding regime. Clean water was also given ad libitum. They were vaccinated against New castle disease (NCD Komorov) and given antibiotics – especially against salmonella spps. Vitamin supplements were also given to ensure good health and improve egg production, fertility and hatchability. The cocks were allowed to run with the hens for a minimum period of two weeks before eggs were collected for incubation. This was to ensure that mating have taken place 18 and to increase the chances of collecting fertile eggs. Eggs were usually collected twice daily – in the mornings (8am) and in the evenings (6pm). Hatchable eggs were collected for 5-7 days before they were transferred to the incubator. The eggs, for each generation, were identified according to pedigree (sire) using an indelible marker pen. They were set in different trays before being put in the incubator. This enabled proper identification on hatching. For the first generation – G0, the natural incubation method as reported by Momoh et al. (2004) was used (here, broody hens were used to incubate the eggs). The still-air handturned electric incubators each with a capacity of 100 eggs were used for the subsequent generations. 3.4 MANAGEMENT OF BIRDS A total number of 294 day-old chicks G0 were obtained using four weekly hatches at Nsukka. These were transferred to Abakaliki for the furtherance of the selection experiment. The day-old chicks’ feathers were painted with different indelible marker pen according to their sire and were brooded together using 100watts bulbs in replicate deep litter pens of 50 birds per pen for four weeks. By the fourth week, they were separated into more replicate pens of 20 birds per pen. At the tenth week, sexing and separation of the males from the females were done using secondary sexual characteristics such as comb size and shape of the tail feather. Of the 221 birds that were still alive in the G0, 134 were females and 87 males. The population size for the different generations of study is presented in Table 1: Table 1: Population Size, Effective Population Size and Change in Inbreeding Coefficient over the Three Generations of Selection Generation Number of Individuals Male Female Population G0 G1 G2 Ne ΔF 67 08 20 114 48 20 Base Population Selected 27.43 Control 0.018 70 11 20 123 55 20 Whole Selected Control 0.014 36.67 140 72 Whole 08 40 Selected 26.67 0.018 20 20 Control l G0= Base Population, G1= Generation One, G2= Generation Two; Ne = Effective population size; ΔF= Change in inbreeding coefficient 19 The whole population for each generation represents the members of the population before selection was done – that is, it is the population from which the selected individuals – those which scored above or equal to the total index score (I) – were obtained. These were the parents of the next generation in which they became the whole population. The control population was maintained separately. The pullets were reared in replicate pens until the eighteenth week when they were randomly assigned to individual battery cages measuring 17 x 32 x 32cm and the cages labeled to monitor individual egg production. Short-term egg production (from first day of lay to ninetieth day of lay) was measured for individual hen. The males were left in the deep litter pens. Feed and clean drinking water were given ad libitum throughout the experimental period. The birds were fed diets formulated by Bendel Feed and Flour Mill Ltd, Benin, Edo State Nigeria. The birds were fed chick ration during the first eight weeks. Grower’s diet was fed between eight and eighteenth week of age and from the eighteenth week of age until after the breeding phase, layers’ diet was fed to the pullets (subsequently hens) whereas the males were maintained on the grower’s diet until the breeding phase when they were fed similar diet as the females. The proximate composition of the feed fed during the experimental period is given in Table 2 below. Table 2: Proximate Composition of Commercial Diets* Diet Type Nutrient (%) CP EE CF Ash ME (kcal/kg) * As labeled on the feed bag. Chicks Mash 21 7.2 4.4 5.8 2750 Growers 14.5 4.8 7.2 8.0 2300 Layers 17 4.0 5.0 12.9 2800 The birds were vaccinated against the major poultry diseases prevalent in the study area. The vaccination schedule used is as shown in Table 3. Table 3: Age Day-old 2 weeks 3 weeks 4 weeks 6 weeks 8 weeks 12 weeks Monthly Vaccination Schedule for the Birds Disease Vaccine Newcastle disease NDV I/O Gumboro disease IBDV Newcastle disease NDV –Lasota Gumboro disease IBDV – booster Fowl pox Pox vaccine Newcastle disease NDV – Komarov Newcastle disease NDVK – booster Newcastle disease NDVK – boost 20 Other medications such as antibiotics and vitamins via drinking water were given as the need arose. Strict sanitary measures were adhered to. 3.5 MAINTENANCE OF A CONTROL POPULATION By the tenth week, 20 males and 20 females were randomly picked from the base population (G0) and the subsequent generations (G1 and G2). These constituted the control population. Similar management procedures were carried out for them as the population for selection. They were reared together in deep litter during the breeding phase as random mating population. The control population, which spanned for three generations (i.e. each generation had its own control population of same age) were used to monitor environmental changes and to estimate genetic change(s) due to selection. 3.6 DATA COLLECTION Data were collected on the following egg production traits: • Age at first egg (AFE): This was taken as the number of days from hatch to the day the first egg was laid provided the second egg was laid in the next ten days (Nwagu et al., 2007b). • Body weight at first egg (BWFE): This was measured as weight of each live pullet on the first day it lays egg. A 5kg- capacity Salter weighing scale was used to measure the body weight of the birds individually. • Weight of first egg (WFE): The weight of the first egg laid by each hen was obtained soon after lay using an electronic balance scale (Mettler P1020N) having a sensitivity of 0.01g. • Total Egg number (TEN): This was taken as the total number of eggs laid by individual layer for ninety days from first lay in so far as the second egg was laid within the next ten days. • Egg weight (AEW): This was taken on individual egg on daily basis from each layer with the aid of an electronic balance scale (Mettler P1020N) having a sensitivity of 0.01g. The average egg weight obtained from individual hens for each week of lay for each population over the short-term period of study was used in the data analysis. 21 3.7 DATA ANALYSIS: 3.7.1 Evaluating the Performance Characteristics Data collected on the egg production traits monitored were subjected to descriptive statistics using SPSS (2001) to obtain the following statistics: means, standard error of mean and coefficient of variation for both the control population and the experimental population for each generation - G0, G1, and G2. Hens that laid less than ten eggs within the short-term period were excluded from the analysis. 3.7.2 Estimation of Genetic Parameters Variance and covariance components for genetic parameters (heritability, genetic and phenotypic correlations) were estimated using mixed model least squares and maximum likelihood computer program PC-1 (Harvey, 1990). This program allows the use of the methodology for unbalanced data. The model used for each trait is the paternal half-sib model as stipulated by Becker (1984). The statistical model was as follows: Yij = μ+ Si + Єij Where Yij = the record of the jth progeny of the ith sire. μ = the overall population mean for the trait being considered Si = random effect of the ith sire Єij =random error / uncontrolled environmental and genetic deviations attributable to the individuals within sire groups. All effects apart from the μ were random, normal, and independent with expectations equal to zero. Table 4: Analysis of Variance Table Source of variation DF SS MS EMS Between sires s-1 SSS MSS σ2W + k1σ2s Progeny within sires n.-s SSW MSW σ2W Where, s = number of sires k1= number of progeny per sire group i.e. for unbalanced data.. n.= total number of individuals 22 The heritability estimate was from above thus – Sire variance component (σ2S) = (MSS - MSW ) / k1 Where, MSw = σ2W heritability, h2S= 4σ2S / (σ2S + σ2W ) Where, h2S = narrow sense heritability/effective heritability. For the paternal – half sib model it was assumed that: a. The population was a random breeding one where maternal effect, dominance epitasis variance were zero. b. No sex linkage and there exist a common environment for all experimental birds. Table 5: Analysis of Covariance Table Source of variation DF SCP MCP EMCP Between sires s-1 SCPS MCPS CovW + kCovs SCPW MCPW CovW Progeny within sires n..-s Where, s = number of sires k= number of progeny per sire group i.e. for unbalanced data.. n..= total number of individuals The genetic correlation rg was estimated as follows: rg = 4 Covs 4 (σ2S(X1) + 4 (σ2S(X2) Where rg = correlation between traits The phenotypic correlation rP was estimated as follows: rP = CovS + CovW 2 (σ W(X1)) + (σ2S(X1)) (σ2W(X2) + (σ2S(X2)) Where CovW = MCPW CovS = MCPS - MCPW k The environmental correlation rg was estimated using the following formulae: and 23 rE = 3CovS(x1,x2) CovW(x1,x2) (σ2W(X1)) - 3σ2S(X1)) (σ2W(X2) - 3σ2S(X2)) Where CovW(x1,x2) = error covariance between traits X1 and X2 CovS(x1,x2) = sire (additive genetic) covariance between traits X1 and X2 σ2W(X1), σ2W(X2) = error variances of traits X1 and X2 respectively σ2S(X1), σ2S(X2) = sire variances of traits X1 and X2 respectively However, the correlations between the various traits studied were estimated using computer programme according to Harvey (1990). 3.7.3 Measurement of Selection Applied 3.7.3a Selection Differential and Selection Intensity Average selection applied was generally measured by the selection differential of parents ΔS, which is the difference in mean performance of selected parents compared with the unselected group from which they came (whole population) over the generations. The value obtained was divided by the average phenotypic standard deviation of the whole population to obtain the standardized selection differential (selection intensity – given in units of standard deviations) for the three generations of study (Ayyagari et al., 1980). The average number of birds examined per generation was 148.7. Number of selected hens ranged from 40-55 and the mean was 47.5, selection pressure did not vary much through out the three generations, and the mean was 27.45% (Table 1). To compare selection applied per unit of time, the parental standard differentials were divided by generation interval – determined from average of parents’ age (Li) in years when offspring were hatched. For this study, the Li of the parents was one year (data from all hens were collected by their ninth month of age, this was used to build the index from which selection of parents of the next generation was made by the tenth month. The hens were thus mated between their tenth to eleventh month and all chicks hatched by the twelfth month). There was non-overlapping of generations as hens were mated to cocks of the same generation. Cumulative selection differential (CΔS) was used to quantify total selection pressure applied. Cumulative selection differential associated with each individual was measured as the average of selection differentials (ΔS) in the parental generation plus the average cumulative selection from each previous generation (G) as follows: 24 G0 = CΔS0 = ΔS0 G1 = CΔS1 = ΔS1 + ΔS0 Similarly, G2 = CΔS2 = ΔS2 + CΔS1 (Leymaster et al., 1979) 3.7.3b Estimation of Expected Direct Response to Selection The expected direct response to selection (ΔGi) in one generation of selection for each trait in the selection criterion was estimated according to Yamada (1958): ΔGi = hi.σgi Where, h is the square root of heritability of the i-th trait σgi is the genetic standard deviation of the i-th trait. 3.7.3c Estimation of Realized Response to Selection The realized direct response per generation is given as the cumulative selection response (RR)i, i.e., deviation of the generation means of the selection criterion from the control means of the same trait. Regression of these cumulated responses on generation numbers was done in order to estimate the magnitude and direction of the average genetic change in the selection criterion per generation. In order to ascertain the direction and magnitude of the response every generation, the cumulative response per generation was broken down according to Ibe et al. (1982) as follows: (RR)i - (RR)i – 1 = Ri Where, Ri = direct response in the ith generation (RR)i = cumulative realized direct response in the ith generation (RR)i - 1= cumulative realized direct response in the (i-1)th generation. To compare selection applied per unit of time, the aggregate genetic gain from the index was divided by generation interval – determined from average of parents’ age (Li) in years when offspring were hatched. The aggregate genetic gain (ΔGAI) or response to selection /year was thus given by: ΔGAI = ΔH/Li 3.8 CONSTRUCTION OF SELECTION INDEX USING THE SELECTION CRITERIA Data collected on body weight at first egg (BWFE), egg number (EN) and egg weight (EW) were used to construct an index of breeding value. These three traits were the selection criteria for the study. The direction of selection was basically positive for all traits. The 25 index was essentially one of multiple regressions in which regression coefficients were derived from a set of three simultaneous equations in accordance with the number of traits being selected. This was used to derive the total score of index (I). Selection index was calculated for each generation of selection. In constructing the selection index to obtain the vector of partial regression coefficients ( b ), estimates of the genetic and phenotypic parameters, and the net worth or relative economic value which were determined for each trait in each generation (Appendices 1-4) were solved using the matrix notation, Pb =Ga according to Becker (1984). Where, P = phenotypic variance-covariance matrix b = vector of partial regression coefficients (weights) G= genetic variance –covariance matrix a = vector of relative economic values. The solution thus became b = P-1Ga. To solve the above matrix, the Mathcad7 Professional was used. The selection index was defined as: I = b1X1 + b2X2 + b3X3 Where, b1, b2 and b3 = standard partial regression coefficients or relative weights for the phenotypic value X of the trait in the index. X1, X2, and X3 = phenotypic values of the traits (BWFE, TEN and AEW respectively). Whereas, the aggregate genotype (H) was: H = a1G1 + a2G2 + a3G3 Where, G’s = the genetic or breeding value of the ith trait ai’s = the relative economic value of the same trait Furthermore, the index coefficients, i.e. the vector b, needed to construct the selection index, were derived such that the correlation between I and H is a maximum. The correlation between I and H is (rIH) = b́ Ga (b́ Pb )½ (áGa ) ½ (Lin, 1978). Heritability of the index was determined according to Pirchner (1983) as the squared correlation between I and H. Expected gains from the index i.e. when selection is on I was obtained using the following equation: 26 ΔH=bHI (Īs - Īμ) = Ī σI = rIH Ī σH where, Īμ and Īs are the mean index values of the population and the selected individuals, respectively, and Ī is the selection intensity factor (i.e., Ī = (Īs - Īμ)/ σI). The genetic gain in the ith index trait due to selection on I is, ΔGi=gí́ b (Ī /σI) where gí́ is a row vector of genetic covariances between ith trait and each component trait incorporated in the index, i.e., the ith row of genetic variance-covariance matrix (Lin, 1978). 27 CHAPTER FOUR RESULTS AND DISCUSSION 4.0 RESULTS 4.1 MEAN PERFORMANCE OF THE VARIOUS EGG PRODUCTION TRAITS STUDIED The mean performance by population for egg production traits studied (age at first Egg – AFE, Weight of first Egg – WFE, Body Weight at First Egg – BWFE, Average Egg Weight – AEW, and Total Egg Number to 90 days of laying –TEN) are presented in Table 6. The influence of both generation and population are presented in Table 7. 4.1.1. AGE AT FIRST EGG Tables 6 and 7 show that in the Light Local Chicken Ecotype (LLCE) used for this study, AFE varied (P<0.05) with respect to population and/or generation of selection. The average AFE for the whole, selected and control populations in the study irrespective of the generation was 160.78± 0.72, 164.28±1.24 and 158.94 ± 0.71 days respectively. Table 7 further reveals that there were significant AFE differences (P<0.05) between the generations both for the whole and selected populations. Such differences were not observed in the various generations of the control population (P>0.05). 4.1.2. BODY WEIGHT AT FIRST EGG (BWFE) The average BWFE of the various populations irrespective of generation were significantly different (P<0.05) from one another and ranged between 834.48g for the control population to 1016.68g for the selected population (Table 6). There were also significant differences (P<0.05) between the BWFE of the G0 and the other generations (G1 and G2) for the whole and selected populations (Table 7). Such differences did not exist (P>0.05) in the control population. 4.1.3. WEIGHT OF FIRST EGG (WFE) There were significant (P<0.05) differences in the WFE with respect to the different populations (whole, selected and control) studied. Birds on the control population had the least WFE whereas the selected population had the highest (Table 6). Table 7 also indicates that the WFE was not influenced by the generation as a result of selection for all populations studied. 4.1.4. AVERAGE EGG WEIGHT (AEW) The average short-term egg weight for the populations was 36.82g with a range of 35.50 – 37.74g. Least AEW was observed in the control population while the highest was 28 obtained from the selected population. Consequently there was significant difference (P<0.05) between the AEW of the control population and those of the other two populations (which were not significant amongst themselves). Results from Table 7 show that in spite of the increase in AEW in the selected population as the generation progressed. There were no significant differences (P >0.05) in the AEW due to change in generation in both the whole and the control populations. 4.1.5. TOTAL EGG NUMBER (TEN) The mean of total egg number from the first day to the ninetieth (90th) day of lay with respect to the three generations of study is also summarized in Table 6. An average of 39.11 eggs was obtained as the short-term egg production of the LLCE. Significant variations (P<0.05) existed amongst the three populations studied. Birds from the control population laid less number of eggs (35.61 eggs) within the short-term period than those from either the selected or whole population (41.51 and 40.21 eggs respectively). Hens in the selected population also laid an average of 1.3 eggs more than those in the whole population (P<0.05). Table 6: Mean (±SE) by population for traits studied Trait Whole Population Selected Control AFE(days) BWFE(g) WFE(g) AEW(g) TEN(eggs) 160.78±0.72a 926.89±10.44a 30.26±0.25a 37.21±0.19a 40.21±0.85a 164.28±1.24b 1016.68±11.58b 31.30±0.42b 37.74±0.31a 41.51±1.59b 158.94±0.71c 161.33±0.83 834.48±10.42c 926.02±12.34 29.44±0.26c 30.33±0.59 35.50±0.24b 36.82±0.41 35.61±0.84c 39.11±1.43 abc Mean Means in the same row with different superscripts are significantly different (P<0.05). AFE – Age at First Egg, BWFE – Body Weight at First Egg, WFE – Weight of First Egg, AEW – Average Egg Weight, TEN – Total Egg Number. 29 Table 7: Mean (± SE) of the Traits Performance by Generation and Population1 Trait2 Population G0 AFE(days) Whole Selected Control 158.84 ±1.06a 159.47±1.97a 158.40±1.13 163.57±1.20b 168.47±1.90b 158.94±0.10 160.34±1.59ab 164.78±2.40ab 159.48±1.47 BWFE(g) Whole Selected Control 855.52±15.77a 962.50±23.33a 880.14±16.72 972.08±16.60b 1024.65±14.18b 831.19±16.02 953.07±4.35b 1062.90±18.06b 792.10±18.85 WFE(g) Whole Selected Control 30.20±0.42 31.51±0.92 29.44±0.37 29.84±0.36 30.62±0.54 29.10±0.45 31.03±0.48 31.92±0.63 29.99±0.66 AEW(g) Whole Selected Control 36.81±0.29 36.51±0.55a 35.27±0.31 37.69±0.31 38.06±0.50b 35.73±0.39 37.13±0.27 38.64±0.49b 35.73±0.59 Generation G1 G2 Whole 33.40±1.23a 42.89±1.35b 44.35±1.76b a b Selected 34.14±2.81 43.20±2.24 47.18±2.36b Control 34.04±1.15 37.06±1.42 37.38±2.21 ab Means in the same row with different superscripts are significantly different (P<0.05). TEN(eggs) 1 LLCE = Light Local Chicken Ecotype AFE =Age at First Egg; BWFE = Body Weight at First Egg; WFE = Weight of First Egg, AEW = Average Egg Weight; TEN = Total Egg Number G0 = Base Population; G1 = Generation One; G2 = Generation Two 2 Table 8 presents means, with standard errors for the selection criteria, and phenotypic regression coefficients of the uncorrected means on generation numbers for both selected and control populations. It is evident that each of the traits was improved substantially in the selected population over the generations than in the control population. The differences between the selected and control populations were significant (P<0.05) in all traits at each generation except for BWFE at the G0. 30 Table 8: Trait Mean (± SE) performance and phenotypic regression coefficients in selected and control populations Generation BWFE(g) Selected Control 0 1 2 962.50±23.33a 1024.65±14.18a 1062.90±18.06a 50.20±6.90 880.14±16.72b 831,19±16.02b 792.10±18.85b -44.02±2.85* 0 1 2 36.51±0.55a 38.06±0.50a 38.64±0.49a 0.92±0.19 35.27±0.31b 35.73±0.39b 35.73±0.59b 0.23±0.13 0 1 2 34.14±2.81 43.20±2.24a 47.18±2.36a 6.52±1.47 34.04±1.15 37.06±1.42b 37.38±2.21b 1.67±0.78 b±SE AEW(g) b±SE TEN(eggs) b±SE ab Means in the same row with different superscripts are significantly different (P<0.05) *P<0.05 BWFE= Body Weight at First Egg, AEW = Average Egg Weight at 90 days of lay, TEN = Total Egg Number at 90 days of lay. b±SE = phenotypic regression coefficients The average phenotypic response per generation although positive in all traits was not significantly different (P>0.05) in the selected population. Similar observations were made for all the selection criteria in the control population except for the BWFE which exhibited a significant negative (P<0.05) regression. Figures 1, 2, and 3 give graphical illustrations of the data in Table 8. Least square means (BWFE) 31 1200 1000 800 600 400 200 0 CONTROL SELECTED 0 0.5 1 1.5 2 2.5 generation Least Square Means (EW) Figure 1: Phenotypic trend of body weight at first egg in 3 generations of selection 39 38.5 38 37.5 37 36.5 36 35.5 35 CONTROL SELECTED 0 0.5 1 1.5 2 2.5 generation Figure 2: Phenotypic trend of egg weight in 3 generations of selection Least Square Means (E N) 32 50 45 40 35 30 25 20 15 10 5 0 CONTROL SELECTED 0 0.5 1 1.5 2 2.5 generation Figure 3: Phenotypic trend of egg number in 3 generation selection 4.2 ESTIMATES OF GENETIC PARAMETERS FOR THE SELECTION CRITERIA 4.2.1 HERITABILITY ESTIMATES The heritability estimates using sire component (paternal half-sib) variance analysis for the selection criteria by population and generation are presented in Table 9. (A) BODY WEIGHT AT FIRST EGG The estimates for BWFE in different populations and generations varied from moderate to high. In the whole population, the estimates were consistently moderate (0.31 to 0.38). The selected population had relatively higher heritability estimates (0.41 to 0.56) for all generations studied. It was, however, observed that despite the fact that the heritability estimate (h2) for the selected population was high, it tended to decrease with each generation of study. The h2 for the control population ranged from 0.33 in G1 to 0.44 in G0. (B) EGG WEIGHT Heritability estimates for average egg weight at the end of the study (90 days from first lay) were moderate to high ranging from 0.21 in the G2 of the whole population to 0.44 in the G0 of the selected population. Interestingly, the heritability estimates of average egg weight in the selected population followed a similar trend as that of the BWFE (decreasing 33 with subsequent generations). Conversely, the h2 for the control population increased with subsequent generations ranging from 0.25 in the G0 to 0.32 in G2. (C) TOTAL EGG NUMBER As shown in Table 9, estimated heritability for total egg number (TEN) was moderate for all generations of study irrespective of the population. Highest heritability estimates 0.28 and 0.26 were obtained in the selected populations for the first two generations (G0 and G1 respectively). The estimate in the last generation of study (G2) was the same as those obtained both in the whole and control populations (0.20). The heritability estimates of average egg weight in the selected population followed a similar trend as that of the BWFE (decreasing with subsequent generations) whereas those of the control were stable. Base Population (G0) Generation One (G1) Generation Two (G2) Traits b Selected Whole Control Selected Whole Control Selected Whole Control BWFE .56±.10 .38±.07 .44±.02 .42±.05 .33±.09 .41±.09 .37±.06 .41±.07 .31±.07 AEW .44±.08 .34±.09 .25±.03 .36±.02 .26±.05 .31±.05 .27±.04 .32±.02 .21±.03 TEN .28±.11 .20±.04 .20±.06 .26±.09 .19±.05 .20±.02 .20±.03 .20±.01 .17±.02 a Weighted value based on paternal half-sib analysis b BWFE= Body Weight at First Egg, AEW = Average Egg Weight at 90 days of lay, TEN = Total Egg Number to 90 days of lay 35 4.2.2 PHENOTYPIC AND GENETIC CORRELATIONS BETWEEN TRAITS The phenotypic and genetic correlations between traits using sire component (paternal halfsib) variance analysis for the selection criteria by population and generation are presented in Table 10. (A) BODY WEIGHT AT FIRST EGG AND OTHER TRAITS The phenotypic correlation between BWFE and AEW for the whole population (0.24) was consistent for the 3 generations of study. Generally, the phenotypic correlation between BWFE and TEN was negative in all generations of study for the whole, selected and control populations although there were variations in their magnitudes. In general, for BWFE and AEW, the genetic correlation did not present any particular trend. It was highest (0.60) in the G1 of the whole population and lowest in the last generation (G2) of the whole population. In the selected population, the phenotypic and genetic correlations between BWFE and AEW were highest in G0 (0.60 and 0.87 respectively) and least in G2 (0.23 and –0.07 respectively). For the Control population, both were highest in the G0 (0.49 and 0.50 respectively) and least in G2 (0.16 and 0.20 respectively). Genetic correlations between BWFE and TEN were moderate to highly negative for all generations of the whole and of the control populations ranging from -0.70 to -0.17 for the phenotypic correlations and -0.74 to -0.40 for the genetic correlations. In the selected population, apart from the G0, where negative correlation though of low value (-0.18) was obtained, genetic correlations in the subsequent generations were positive but very low (0.09 and 0.02 for G1 and G2 respectively). (B) EGG WEIGHT AND TOTAL EGG NUMBER The estimated phenotypic correlations between AEW and TEN were low to moderate and negative for all generations and populations. Similar trend was however not observed in the case of genetic correlations. In the whole population for instance, the estimated genetic correlation between the two traits was very low and positive in G0 and G2 (0.01 and 0.05, respectively) whereas it was high and negative (-0.84) in the G1. In the selected population, genetic correlation was negative in G0 and G1 (-0.39 and –0.91, respectively) but in G2 it was moderately positive (0.33). Traitsa BWEN BWEW EWEN Generation Selected rg rp re Zero (G0) Whole Control rg rp re rg rp -.18 -.24 -.29 -.61 -.29 -.18 -.40 -.19 -.38 -.04 -.23 -.28 .87 .34 .50 -.07 .23 .60 .34 -.39 -.23 -.14 a .24 .18 .01 -.26 -.36 re .49 .45 .07 -.16 -.22 Generation One (G1) Selected Whole rg rp re rg rp -.74 -.18 -.02 .41 .60 -.91 -.60 .32 re .25 .13 -.84 -.23 -.09 Control rg rp re G Sel rg -.54 -.17 -.05 .02 .33 .29 .26 .13 -.34 -.30 -.29 .33 BW= Body Weight at First Egg; EW = Average Egg Weight at 90 days of lay; EN = Total Egg Number to 90 days of lay rg = Genetic correlation; rp= Phenotypic correlation; re= Environmental correlation 37 4.3 MEASUREMENT OF SELECTION APPLIED 4.3.1 SELECTION DIFFERENTIAL, SELECTION INTENSITY AND SELECTION RESPONSE The realized selection differentials, phenotypic standard deviations, selection intensities and the selection responses from G0 to G2 for the selection criteria - BWFE, AEW, and TEN are shown in Table 11. The selection intensity measured as standardized selection differential were 0.105 for TEN, 0.160 for AEW and 0.748 for BWFE over the three generations of selection. There were large variations between the direct response to selection and the realized response to selection. It is evident that each of the traits was improved substantially in the selected population over generations than in the control population. The realized response by G2 for BWFE, AEW and TEN was 270.80g, 2.91g and 9.80 eggs respectively. The estimated realized genetic progress (b) from the three generations of study (obtained by regressing realized response on generation) was 94.22g, 0.834g and 4.85 eggs for BWFE, AEW and TEN which is close to the total expected index response 93.24 and 0.83 for BWFE and AEW respectively. Although the values for the estimated realized response and the estimated index response for TEN over the three generations were close, they differed in magnitude (4.85 and -4.34, respectively). Figures 4, 5 and 6 represents linear regression graphs for realized response on generation for BWFE, AEW and TEN respectively. Traitsb Gen. RSD σP BWFE(g) G0 106.98 AEW(g) I CSD ΔGi RR CRSR 160.35 106.98 60.94 82.36 82.36 G1 52.57 G2 109.83 Average 89.79 94.65 105.19 120.06 159.55 269.38 29.34 38.92 193.46 270.80 275.82 546.62 G0 -0.30 3.3 -0.30 1.92 1.24 1.24 G1 0.37 3.32 0.07 0.70 2.33 3.57 G2 1.51 Average 0.52 3.24 3.28 1.58 0.88 2.91 6.48 0.748 ER 94 0.16 0.8 TEN(Eggs) G0 0.74 11.6 0.74 2.32 0.10 0.10 G1 0.31 12.93 1.05 2.06 6.14 6.24 G3 2.83 12.35 3.88 2.47 9.80 16.04 12.29 0.105 Average 1.29 a LLCE = Light Local Chicken Ecotype ; b BWFE= Body Weight at First Egg; AEW = Average Egg Weight; TEN = Total Egg Number Gen. = Generation; RSD = Realized Selection Response; σP = Phenotypic Standard Deviation; i = Selection intensity; CSD = Cumulative Standard Deviation; ΔGi = Expected Direct Response; RR = Realized Response; CRSR = Cumulative Realized Selection Response; ERR (b) = Estimated Realized Response over three generations; EIR = Expected Index Response 4.8 Body Weight at First Egg (g) 39 300 250 200 150 100 50 0 0 0.5 1 1.5 2 2.5 2 2.5 Generation Average Egg Weight (g) Figure 4: Regression of BWFE response on generation number 3.5 3 2.5 2 1.5 1 0.5 0 0 0.5 1 1.5 Generation Figure 5: Regression of AEW response on generation number Egg number (eggs) 40 12 10 8 6 4 2 0 0 0.5 1 1.5 2 2.5 Generation Figure 6: Regression of TEN response on generation number 4.4 SELECTION INDEXES USED FOR THE SELECTION OF LLCE HENS The following indexes were determined: I = 0.341X1 – 0.496X2 + 3.006X3 for G0; I = 0.265X1 – 0.668X2 + 1.866X3 for G1 I = 0.353X1 – 0.600X2 + 0.848X3 for G2 Where, X1, X2, and X3 are BWFE, TEN and AEW, respectively. The indexes for the three generations of study were not similar because the economic values of the trait varied from generation to generation and the genetic parameters that were used for constructing them also varied greatly (Appendices 1-4). The estimated index score, selection intensity factor, heritability of index, genetic gain in aggregate genotype and correlation of index and aggregate genotype, expected annual genetic response and generation interval are presented in Table 12. The selection intensity estimate with regards to the index using the selection criteria ranged from 0.59 in G0 to 0.98 in G2 (Table 12). The expected response per year resulting from the index represented as expected annual genetic progress (ΔGAi) in Table 12 indicated that the largest gain was in G2 for the present study whereas the least was in G1. This expected annual genetic progress was equivalent to the genetic gain in the aggregate genotype because the generation interval (Li) defined as the average age of parents in years when their offspring were hatched was one year (12 months). The ratio between these two generations was 1.856 (37.08/19.98), suggesting that selection in G2 was almost twice as efficient as the latter in this study. Comparison of 41 expected annual genetic gain between the last generation (G2) and the G0 resulted in a ratio of 1.037 (37.08/35.77). Table 12: Estimated Index Score (Selected Is and Whole Iμ population), Selection Intensity Factor Ī, Heritability of Index h2, Genetic Gain in Aggregate Genotype ΔH and Correlation of Index and Aggregate Genotype rIH, Expected Annual Genetic Response (ΔGAi) and Generation Interval Li Generation Is Iμ Ī h2 ΔH rIH (ΔGAi) Li 0 426.8 390.92 0.59 0.39 35.77 0.623 35.77 1 1 313.7 293.72 0.66 0.32 19.98 0.563 19.98 1 2 315.38 278.3 0.98 0.38 37.08 0.612 37.08 1 4.5 DISCUSSIONS 4.5.1 MEAN PERFORMANCE OF THE VARIOUS EGG PRODUCTION TRAITS STUDIED The overall differences between the selected population and the other populations in AFE, BWFE, WFE, AEW and TEN as observed in Table 6 could largely be attributed to effect of selection on these traits. The results further show that hens from the control population tended to attain sexual maturity about five days earlier than those from the selected population. However, findings from both populations are still within the range of 153 – 206 days reported in literature for unimproved local chickens both in Nigeria and other countries (Choprakarn et al., 1998; Adedokun and Sonaiya, 2001; Demeke, 2003, Tadelle et al., 2003; Khalil et al., 2004). Nwosu and Omeje (1985) noted that it takes about five and half to six months for the local chicken to mature. Tule (2005) worked with a random-bred population of LLCE and reported an average AFE of 156.5 ± 0.70 days and 155.85 ± 0.62 days for LLCE hens raised in the deep litter and battery cage systems of management respectively. This is not so different from the result obtained from the control population in the present study. Invariably, the discrepancies between the AFE for birds in the whole as well as the selected population and those in the control population are most probably as a result of the application of selection in the former populations which affected their performance. 42 The result in Table 6 indicated an average WFE of 30.33g for the LLCE used for this study. Nwosu and Omeje (1983) reported a mean WFE of 25.98g. Tule (2005), working with the grandparents of the base population of this study, obtained a mean WFE of 25.70g. The findings of this study show that the mean WFE obtained was at least 14.34% greater than these reports. Presumably, this difference could be traced to effect of selection on these traits over the generations. WFE increased with increase in AFE and BWFE for the whole and selected populations over the generations of selection. The present findings are in line with the works of Steigner et al. (1992) and Anthony et al. (1993) on the Japanese quail. These authors observed that in Japanese quail selection for heavy body weight resulted in delayed sexual maturity i.e. increase in age at first egg (AFE). Body weight, generally, has been shown to be highly responsive to selection in chickens such that genetic improvement for growth has resulted in increase in egg weight and age at first egg/sexual maturity (Barbato, 1999). Soller et al. (1984) investigated the minimum weight for onset of sexual maturity in chickens and suggested that the AFE is highly correlated with body weight. Oruwari and Brody (1988) also concluded that an interaction/correlation exists between chronological age and body weight at the onset of sexual maturity. The report of Tule (2005) shows that the average BWFE of the LLCE raised in battery cage from the 18th week of age was 836.61 ± 2.77g. This result is close to the 855.52 ± 15.77g and 880.14±16.72 obtained in the G0 (prior to selection) of the whole and control populations, respectively. However, the selected population exhibited a great variation in BWFE when compared with the control population. The variation was 8.56%, 18.88% and 34.19% above that of the control for G0, G1 and G2 respectively. The BWFE for all population and/or generation of study is lower than 1447.1g averaged over both sexes for medium ecotypes of Tanzania under intensive management (Lwelamira et al., 2008) and a range of 1600 – 2200g obtained for South African local chickens by ARC (2005). However, the BWFE of the selected population by the G2 was close to corresponding weights of the Tanzanian medium ecotype chicken under extensive management (Lwelamira, et al. 2008). Although the selected population had higher BWFE than the control, the current findings attest that the Nigerian local chicken has poor BWFE when compared to the improved stocks. This could, more or less, be attributed to the effect of selection for high BWFE over the three generations. Moreover, BWFE for this ecotype could probably be further improved following genetic improvement through selection. The high heritability 43 estimates obtained for BWFE in the three generations of study clearly indicate the existence of substantial amount of additive genetic variance in this population with regard to BWFE. The results of the current study show that the light local chicken ecotype had lower egg weight than those reported for other breeds such as White Leghorn (45.5g), Fayoumi (42.24g) and RIR (43.4g) at 90 days of lay (Mekky et al., 2008). The values obtained both in the control and selected populations were also lower than those of about 40g given by Fayeye et al. (2005) for the Fulani ecotype. However, the mean egg weight of the LLCE used for this study is very close to the findings of Soltan (1991) for the Fayoumi (37.3g) and Baladi (39.2g) chickens. The results of the present study with regards to total egg number indicates that egg number of the local chicken after three generations of selection was an average of 47.13 eggs for the selected population for the short term (90 days from first lay) production period investigated. Abdou and Kolstad (1984) reported an average egg number of 44 eggs for Fayoumi and 41eggs for white Baladi. Although Nwosu and Omeje (1985) stated that the productive ability of the Nigerian local chicken (under extensive management) ranges from 40-80 eggs. They, however, noted that the local chicken real genetic potential for production to be about 125 eggs. Momoh et al. (2007) reported a total egg number of about 144 and 136 eggs respectively for the light and heavy ecotypes of the Nigerian local chicken. Gowe (1970) in his work on long-term selection for high egg production in 2 strains of Leghorn reported that selection for part-period hen-housed egg production was effective in increasing the performance of the selected strains. Bohren et al. (1970) and Khalil et al. (2004) noted a positive relationship (0.80 and 0.81 respectively) between short term or part – period egg production and long-term production. If such association between part-term and long-term production as reported by the authors above does exist, it invariably means that the local chicken most likely would produce up to 125 eggs or more at the end of a production cycle. Hence, in agreement with Adebambo (1999) the Nigerian Local Chicken could be a suitable genetic material for the development of layer strains for the Nigerian market. 4.5.2 HERITABILITY ESTIMATES There is indication that traits selected for in this study were moderately to highly heritable. The heritability estimates especially those of the selected population are on average close to the findings of Lwelamira et al. (2009) who worked on two Tanzanian chicken ecotypes. They reported heritability estimate of 0.45±0.09 and 0.43±0.07 for Kuchi chicken and Medium ecotypes respectively. The relatively high heritability estimates obtained for 44 BWFE particularly in the selected population corroborates the results obtained by Tufvesson et al. (1999) and Szwaczkowski et al. (2003). Jorjani et al. (1993) obtained a range of 0.54 to 0.65 estimates for body weight. Momoh and Nwosu (2009) worked with a Nigerian heavy chicken ecotype and reported that values of heritability estimates of body weight of the heavy ecotype increased from 0.18 at 4 weeks to 0.43 at 8 weeks and thereafter declined to 0.16 at the 16th week to rise again to 0.30 at the 20th week. They concluded that on the average, the body weight of the heavy ecotype could be described as being lowly to moderately heritable and suggested that the heavy ecotype has dual penitential to be selected either as a meat- type or egg- type bird. Nordskog (1981) reported heritability estimate of 0.8 for average short term rate of egg production in Light breeds. Venkatramaiah et al. (1986) reported a moderate to low estimates for egg number in four lines of Single Comb White Leghorns. The pooled estimates ranged from 0.127 to 0.331. Oni et al., (2000) reported that estimates of heritability pooled over generations from sire, dam and sire and dam components of variance were 0.13, 0.16 and 0.15 for egg number for 280 days of age for a strain of Rhode Island chicken under selection. Edriss et al. (1999) cited by Vali (2008) examined heritability coefficient of laying characteristics of indigenous chickens of Iran using sire variance component and reported estimates of 0.26, 0.86, and 0.80 for age at sexual maturity, number of eggs at 34 weeks of age and egg weight from maturity age to 34 weeks respectively. The reported estimates of heritability for egg number varied from 0.11 to 0.53 (Francesh et al., 1997; Nurgiartiningsih et al., 2002, 2004; Szwaczkowski, 2003). Luo et al. (2007) reported heritability estimates of cumulative egg numbers within the range of 0.16 to 0.54 in their study using broiler breeder strains and suggested that the result indicates a moderate to low additive genetic variance for egg production in broiler breeders. Lwelamira et al. (2009) also reported heritability estimates for egg number (90th day of lay) as 0.31±0.05 and 0.32±0.06 for Kuchi chicken and Medium ecotypes respectively. Estimates of heritability for egg number for all population and generation in this study falls within this range. Kinney (1969) obtained an average heritability for early egg weight in light breeds – using white leghorn, its classical and reciprocal with Rhode Island Red – as 0.45, 0.53, 0.45 and 0.52. These were obtained respectively from sire, dam, and sire and dam covariance components, and daughter-dam regression and indicated that in light breeds, egg weight had high heritability. Nordskog (1981) reported heritability estimates of 0.45, and 0.57 for early egg weight, and 0.46 and 0.58 for mature egg weight in Light breed and Heavy breeds respectively. Oni et al. (2000) reported that estimates of heritability pooled over generations 45 from sire, dam and sire and dam components of variance were 0.24, 0.20 and 0.24 for average egg weight for a strain of Rhode Island chicken under selection. The heritability estimates for egg weight obtained for the different populations and over three generations in the present study with LLCE using sire component of ranged from 0.21 to 0.56 indicating a moderate to high additive genetic variance for egg weight in the LLCE. Of importance also is the heritability of the index, which ranged from 0.32 in G1 to 0.39 in G0. Such moderate h2 estimates indicate that the traits as they appeared in the index were most probably passed on from the parents to their progenies without much influence by the environment. When heritability of a trait is high, the correlation between phenotype and the genotype of the individual on the average should also be high and selection on the basis of the individual’s own phenotype should be effective (Kabir et al., 2007). Low heritability estimate, on the other hand, is an indication of low correlation between the genotype and phenotype. Hence, low heritability estimates mean that variations due to additive gene action are probably small; rather non-additive gene action such as over dominance, dominance and epistasis may be important. Environmental (high temperature and humidity) and poor management conditions are known to increase the residual variance and decrease the heritability estimates (Soller et al., 1965). Hence, producers should select to improve traits with low heritabilities when economic circumstances justify the attention. In addition, lowly heritable traits which are of substantial economic value should always be targeted for improvement through better environmental conditions. Traits of low heritability can be selected for successfully by using selection methods such as index selection as attested by the present findings with regards to egg number. Moreover standardized environmental conditions can actually increase heritability by reducing the non-genetic differences between animals. The heritability estimates for the selection criteria had larger values in the selected population than in the control population. This is contrary to the report of Fairfull and Gowe (1990) that heritability estimates from selected strains are generally lower than those from unselected strains. They worked on Leghorns and noted that there may be some differences in heritability estimates among breeds. The variation in the heritability estimate of the selected populations and the control populations could be attributed to sampling error due to small data or sample size. The number of individuals used for the estimation of heritability in the control population was smaller than that of the selected population. However, of much relevance is the fact that the estimates were moderately to highly heritable for all traits selected for in this study – even in the control population. 46 4.5.3 PHENOTYPIC AND GENETIC CORRELATION BETWEEN TRAITS The pattern of differences in the genetic and phenotypic correlations between selected and control population was not consistent as that observed in the case of the heritability estimates. This is in line with Fairfull and Gowe (1990) who attributed this variation partly due to the fact that genetic correlations are generally estimated with less precision than heritabilities. However, the correlation estimates appeared to be more realistic in the control populations. This was especially true for the genetic correlations involving BWFE/AEW, BWFE/TEN, and AEW/TEN in G1 and G2. Van Vleck (1968) concluded that larger biases in estimates of genetic correlations result when selection is intense. This could have influenced the estimates of the selected population in this study. The results of this study affirm those of several reports stating negative correlations (phenotypic and/or genetic) between body weight at first egg and egg number, and between egg weight and egg number or egg production (Festing and Nordskog, 1967; Besbes et al., 1992; Jeyarubau et al., 1996; Francesch et al., 1997). The genetic correlation between AEW and TEN in the present study, however, was positive in the G2 of the selected population. This change in magnitude could be attributed to the selection method applied in the study. Here, selection was based on an index score where, the selection criteria, BWFE, AEW and TEN were all selected for in a positive direction. In other words, only hens which ranked above or were equal to the index score were selected as parents of the next generation. Such selection invariably tends to increase the gene frequency of the favoured genes, which in the course of recombination could have been transmitted together as linked genes. This, most probably, translated to the maximum performance observed in the selected population. This should be of much interest to the breeder of LLCE, for perhaps with continuous selection and breeding, the LLCE could become the White Leghorn of Nigeria! Some authors have also reported positive associations between the selection criteria (that is body weight at first egg, egg number and egg weight) in exotic hens (El-Hossani, 1978; Oluyemi, 1978; Oni et al., 1991). Fairfull and Gowe (1990) also reported significant positive genetic correlation between egg number and egg weight in unselected control lines of white leghorns. 4.5.6 MEASUREMENT OF SELECTION APPLIED The selection response of the selection criteria appeared to increase with each generation of study. However the increase was somewhat not linear for the three generations of selection. The average response per generation though positive in all traits was not 47 statistically significantly. Schmidt and Figueriedo (2005) suggested that such observation implies that there was no genetic change after the selection was started. Hill (1971, 1972a and b) showed that genetic drift, individual measurement sampling, genotype-environment interactions and time trends in environment and natural selection are the possible causes of variable response in a selection experiment. However, it should be stressed that the effects of selection on responses in the present study could not possibly have been affected by drift since selection was carried out only in three generations. Selection response tends to decrease and eventually disappear in long-term selection applied to a closed population as a result of increase in homozygosity or genetic drift due to inbreeding (Nwagu et al., 2007a). Zieba and Lnkaszweicz (2003) observed a higher response in V44, regardless of its smaller population size, and suggested that this may indicate more space left for improving this trait in the paternal line. There was an average increase of 1.58g in egg weight for the LLCE due to selection in the selected population and a genetic gain of 2.91g when compared with the control population. Nordskog and Festing (1962) in Fayoumi showed that selection for egg weight resulted in a 4g increase in egg weight. El-Wardany (1987), cited by Mekky et al. (2008), recorded an increase of 2g in egg weight in the course of developing an egg strain line in Norfa chicks through two generations. Results in Table 11 reveal that the total response for each trait using the index compared favourably with the realized response per generation estimated using the regression of direct response on generation number. For instance the response/generation was 94.220g and 0.835g for BWFE and AEW respectively whereas the total response from the index for BWFE and AEW over the three generations was 93.24g and 0.83g respectively. The expected response for TEN as estimated from the index was negative (-4.34eggs) over all generation of study. This could be attributed to the negative correlations between this trait and the other selection criteria which were used in the construction of the selection index. Although negative response was expected in egg number since the total index response over the three generations was negative such was not observed in the realized response (4.85eggs). This could be due to complimentary or additive effect of using the selection index method. Gowe (1977) reported genetic gains of 4.8, 3.7 and 12.4 eggs in hen housed egg number to 273 days of age from five generations of selection in three White Leghorn lines selected on the basis of an index. Soltan (1997) obtained a significant selection response or genetic progress/improvement for egg number (ninety-days from first lay) in Baladi fowl of 7.1 eggs in three generations of mass selection. Venkatramaiah et al. (1986) reported an 48 average genetic change per generation of 2.16 eggs in egg number and 146g of egg mass in egg mass in selected sublines of White Leghorn. The Table further reveals that of the three traits comprising the aggregate genotype, BWFE reflected the largest expected response in all three generations of study. This was contrary to expectation because record of BWFE had the least economic weight associated with it and, therefore, should not have dominated the index. Hicks et al. (1998) reported that a trait that had the largest economic weight associated with it tended to dominate the index. However, the discrepancy between the report of Hicks et al. (1998) and the present study could perhaps be traced to the larger genetic and phenotypic variances of BWFE when compared to the other traits noting that the coefficient values of an index is more or less a function of both the phenotypic matrix and genotypic matrix (Lin, 1978). Of particular interest were the negative values associated with expected response for egg number in all the generations of study. This response could have as well resulted from the negative genetic and phenotypic correlation between egg number and the other traits. Generally, the simultaneous inclusion of the three traits (BWFE, AEW, and TEN) in the selection index, while selection was in a positive direction for the respective traits, improved the performance of the selected individuals in these traits. Some authors have noted similar results. Sharma et al. (1983) showed that selections on the basis of an index (using data on body weight at 8 weeks, egg production to 300 days, and percent hatchability) was relatively more efficient than tandem selection or independent culling levels selection for all traits except hatchability. Comparisons between index selection and mass selection for various traits such as body weight at 8 and 20 weeks of age, 35-week egg weight, age at sexual maturity, and egg production to 260 days of age in White Leghorn strains were made by Verma et al. (1984). For aggregate genetic response, index selection was found to be 2.76, 3.33, 13.66, 1.32 and 1.53 times more efficient than direct selection for egg production, egg weight at 35-week of age, initial egg weight, 20-week body weight and 8-week body weight respectively. Singh et al. (1984) compared various selection indices to improve egg production in White Leghorn flocks when 10% of the cockerels were selected in each population, selection of 40% of the females based on index selection was found to give better genetic response than when selection with different intensity was made on individual traits. Ayyagari et al. (1985) recorded similar observations in another White Leghorn population and by Makarechian et al. (1983) while working with indigenous chickens of southern Iran. 49 CHAPTER FIVE CONCLUSION AND RECOMMENDATION The local chicken constitutes 80% of the poultry birds in Nigeria and plays an important role in household food supply. The poor growth, small body and small egg size of this chicken have made them to be non-alternative and non-desirable in a competitive economic sector. However, the dramatic size contraction of the local chicken due to replacement with cosmopolite improved breeds shows the need for greater resources conservation. The genetic resources base of the local chicken could form the basis of improvement and diversification to produce breeds adapted to local conditions. The Nigeria local chicken possess genetic raw material for layer chicken production. Such gene pool can be improved or modified through selection and crossbreeding. Although crossbreeding leads to the creation of more heterozygotes and consequently to greater genetic variation in the population, selection may increase homozygosity in a particular flock, and determine genetic gain. Furthermore, unlike crossbreeding which is a faster way of achieving progress, selective breeding, though slow, is an essential tool in developing and maintaining a strain/breed (because genetic improvement through this method is permanent and cumulative). Thus, in order to incorporate the local chicken as a parent breed to produce strains of chicken that are adaptable to the local environment as well as achieving the much desired goal of making Nigeria self sufficient in the sourcing of poultry breeding stock and boosting her poultry industry, there is need for selective breeding. To achieve this, there should be adequate information on genetic parameter estimates with regards to growth performance and egg production traits of the local chicken. Of immense advantage is early estimation of the genetic parameter estimates of these production traits using efficient selection method(s) in order to shorten generation interval and enhance rate of genetic gain per unit time. The results of the present study revealed that heritability estimates for all traits in all generations were moderate to high in both the selected and control populations. The heritability of the index was also moderate (ranging from 0.32 in G1 to 0.39 in G0). Such moderate to high heritability estimates indicate high additive genetic variances, implying that these traits were passed on from the parents to their offspring. Generally, moderate to high positive genetic and phenotypic correlations were observed between BWFE and AEW in all populations of study. The genetic correlation and phenotypic correlation between BWFE and EN and between AEW and EN was moderate to highly negative for all generations and 50 populations of study. There was, however, a change from expected negative genetic correlation between AEW and TEN to positive genetic correlation in the last generation of study in the selected population. A cumulative selection differential of 269.38g, 1.58g and 3.88 eggs were obtained for BWFE, AEW and TEN respectively over the three generations of study. Selection response for all the selection criteria increased over the generations in a fairly linear manner suggesting genetic improvement due to selection. Accurate estimates of genetic and phenotypic parameters are a pre-requisite for the establishment of a sustainable genetic improvement programme. Since heritability estimates and genetic/phenotypic correlations in this study were moderate to high, this will be an encouraging factor for intense selection within the local chicken population before being crossbred with improved stocks in order to achieve new breed(s). Moreover, it is evident that the simultaneous inclusion of BWFE, AEW, and TEN in the selection index generally improved the performance of selected birds over the generations in the Light Local Chicken Ecotype. This virtually suggests that selection based on an index should be applied in breeding programmes for the development and/or improvement of egg production traits in the LLCE. To conserve the diversity of ecotypes like the Light Local Chicken Ecotype and other local chicken ecotypes/strains which are now becoming increasingly vulnerable (their numbers are declining progressively) because farmers are discouraged from keeping them due to their low market value, it is essential to take a number of measures sooner than later. Among the measures that can address this challenge are to: (1) design and implement appropriate community based genetic improvement programme for local chicken ecotypes. (2) maximize the benefits that farmers could get from chicken farming - government and non-government organizations should strengthen ongoing efforts and start new initiatives aimed at facilitating: • access to adequate concentrate feeds at affordable prices all year round; • expanding cheap and effective extension and veterinary services (including training and deploying farmer vaccinators); • introducing environmental and user-friendly artificial incubators and brooders (run by solar, kerosene); • linkage /access to market and up to date information on poultry production practices. • improving credit services could also address the growing population pressure on the 51 limited available farm land and youth unemployment that is growing is rural areas by encouraging such groups to engage in poultry production- especially the local strains/breeds which is more economical to manage and profitable. (3) Finally, there is need for a continuous improvement of the local chicken over several generations to obtain a pure breed. It will, therefore, be worthwhile that the Federal Government, various NGOs, agricultural firms/parastatals and research institutes/universities collaborate on modalities of funding and sustaining breeding programmes with regards to this bird – otherwise the little improvement obtained in the course of research studies will be lost and the gene pool goes back to status quo. 52 REFERENCES Abdou, F.H. and Kolstad, N. 1984. A study on the performance of Norwegian and Egyptian breeds of laying hens and their crosses XVII Worlds Poult. Cong. Helsinki, Finland. Adebambo, O.A. 1999. Improving the potential of Nigeria livestock genetic resources in the new millennium. In: Genetics and Food Security in Nigeria in Nigeria in the twenty-first century. Genet. Society of Nig. 175-186. Adebambo, O.A., Ikeobi, C.O.N., Ozoje, M.O., Adenowo, J.A. and Oshinowo, O.A. 1999. 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Each hen consumed on average 6261.60g of feed between day of 1st egg to 90th day of lay. Then feed utilization given as FCR = 6261.60 = 47.32g feed/g wt gain 132.32 25000g (25kg) of layers mash costs N920 Therefore, 47.32 x 920 = N1.74 = Economic weight of BWFE 25000 (B) TEN The economic weight of TEN is the additional gain to be derived from an increase of 1% in the laying performance. For G0 Whole population, average egg number was 33.40 eggs over 90 days of lay. An increase of 1% over same period will give additional 0.334 eggs = 33.734 eggs or approximately 33.73 eggs in 90days. A crate of the local chicken egg sold for N250. Therefore each egg cost N8.33k. Then 0.33egg x 8.33 = N2.749. Thus, 0.334 67 additional egg will give additional gain of N2.749k. The economic weight of egg number = N2.749k (C) AEW The economic weight of egg weight is the additional gain to be realized by an increase of 1g in the weight of each egg. For G0 Whole population, each hen produced a mean egg number of 33.40eggs. 1g increase will give additional 33.40g, with AEW of 36.81g/hen, 33.40 eggs will give 33.40 = 0.9074eggs. 36.81 Each egg cost N8.33, additional 0.9074eggs will cost N7.56k The relative economic weight was then obtained by dividing the economic values of each trait with the least economic value. Thus for G0, the relative economic value (weight) for BWFE, TEN and AEW were 1, 1.5798, and 4.3448. The relative economic weights of the traits for the G1 and G2 were determined by similar reasoning with appropriate adjustments in prices of products and cost of feed as occasioned by the prevailing production and market situation. 68 APPENDIX 2 MATRIX SOLUTIONS TO OBTAIN SELECTION INDEX FOR G0 Phenotypic matrix (P) 25713.25 Genotypic matrix (G) -543.730 124.335 134.606 -10.206 10.870 9771.035 -312.8857 64.23569 26.9212 0.099785 3.6958 69 a= 1 Ga = 9557.4438 1.579 -285.9214 4.344 80.8352 b= 0.347 -0.496 3.006 APPENDIX 3 MATRIX SOLUTIONS TO OBTAIN SELECTION INDEX FOR G1 Phenotypic matrix (P) 8958.28 Genotypic matrix (G) -217.332 76.490 167.120 -9.752 10.987 2777.0668 -201.6506 26.7392 48.0288 -6.5979 2.3073 70 a= 1 Ga = 2663.5727 1.529 -187.5316 4.058 47.3014 b= 0.353 -0.6 0.848 APPENDIX 4 MATRIX SOLUTIONS TO OBTAIN SELECTION INDEX FOR G2 Phenotypic matrix (P) 11065.44 Genotypic matrix (G) -90.918 81.998 152.571 -4.163 10.542 4094.2128 -166.1155 30.5142 10.7952 0.4662 2.8463 71 a= b= 1 Ga = 3896.2294 1.445 -120.2177 3.891 22.5436 0.353 -0.6 0.848 RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR COV CORRELATION 1 1 1 EGGNUM EGGNUM 74681.483831 128.53955909 1.0000 1 1 2 EGGNUM BWFE -275102.042253 473.49749097 -.2724 1 1 3 EGGNUM EGGWT -5903.322284 10.16062355 -.2810 1 2 2 BWFE 23502.09967332 1.0000 1 2 3 BWFE 111.66049637 .2284 1 3 3 10.17312100 BWFE 13654719.910200 EGGWT EGGWT EGGWT 1.0000 64874.748388 5910.583301 LEAST-SQUARES ANALYSIS OF VARIANCE EGGNUM D.F. SUM OF SQUARES MEAN SQUARES SOURCE PROB TOTAL 591 79417.350254 TOTAL REDUCTION 10 4735.866423 3.684 .0001 MU-YM 1 .218614 .002 .9671 SIRE 9 4735.866423 4.094 .0000 REMAINDER 581 74681.483831 MEAN = 33.39594 ERROR STANDARD DEVIATION = SQUARED = .060 R = .244 SOURCE PROB TOTAL TOTAL REDUCTION .0000 MU-YM .004 .9505 SIRE .0000 D.F. BWFE SUM OF SQUARES F 473.586642 .218614 526.207380 128.539559 11.33753 CV = 33.95 R MEAN SQUARES 591 10 15170817.597293 1516097.687093 151609.768709 1 90.556285 90.556285 9 1516097.687093 168455.298566 F 6.451 7.168 TOTAL 591 6507.038917 TOTAL REDUCTION 10 596.455616 5.863 .0000 MU-YM 1 .029744 .003 .9569 SIRE 9 596.455616 6.515 .0000 REMAINDER 581 5910.583301 MEAN = 36.80865 ERROR STANDARD DEVIATION = SQUARED = .092 R = .303 59.645562 .029744 .272846 10.173121 3.18953 CV = 8.67 R VARIANCE AND COVARIANCE COMPONENT ESTIMATES FROM DIRECT ANALYSIS K FOR RANDOM EFFECTS COMPONENT (SIRE) = 58.9870 DEGREES OF FREEDOM = 9. SS, CP, MS, MCP, VARIANCE AND COVARIANCE COMPONENTS JOB ROW COL RHM COMPONENTS 1 1 1 EGGNUM 6.74161485 1 1 2 EGGNUM -78.05339846 1 1 3 EGGNUM .02427021 1 1 1 RHM SS OR CP MS EGGNUM 4735.86642328 526.20738036 BWFE -45698.71916862 -5077.63546318 EGGWT -78.56096499 -8.72899611 2 2 BWFE 2457.37418378 2 3 BWFE 16.17440374 BWFE 1516097.68709275 168455.29856586 EGGWT 9591.66447123 1065.74049680 3 3 .95105191 EGGWT 596.45561630 66.27284626 EGGWT APPENDIX 6 OR COV 160.85277625 1.0000 1 1 2 EGGNUM BWFE 170.79711466 -.1476 1 1 3 EGGNUM EGGWT 8.21700129 -.1999 1 1 1 2 2 BWFE 8329.07311562 2 3 BWFE 66.80798945 BWFE 1.0000 EGGWT .2259 3 3 10.50254435 EGGWT 1.0000 EGGWT -174725.448299 - -8405.992321 - 8520641.797277 68344.573212 10744.102870 LEAST-SQUARES ANALYSIS OF VARIANCE EGGNUM SOURCE D.F. SUM OF SQUARES MEAN SQUARES F PROB TOTAL 1033 172467.856728 TOTAL REDUCTION 10 7915.466624 791.546662 4.921 .0000 MU-YM 1 .894546 .894546 .006 .9406 SIRE 9 7915.466624 879.496292 5.468 .0000 REMAINDER 1023 164552.390104 160.852776 MEAN = 42.89158 ERROR STANDARD DEVIATION = 12.68277 CV = 29.57 R SQUARED = .046 R = .214 BWFE D.F. SUM OF SQUARES SOURCE PROB TOTAL 1033 9244941.916748 TOTAL REDUCTION 10 724300.119470 8.696 .0000 MU-YM 1 468.850363 .056 .8125 SIRE 9 724300.119470 9.662 .0000 REMAINDER 1023 8520641.797277 MEAN = 972.08258 ERROR STANDARD DEVIATION = SQUARED = .078 R = .280 EGGWT MEAN SQUARES F 72430.011947 468.850363 80477.791052 8329.073116 91.26376 CV = 9.40 R .017 .8958 SIRE 9 620.824526 6.568 .0000 REMAINDER 1023 10744.102870 MEAN = 37.68538 ERROR STANDARD DEVIATION = SQUARED = .055 R = .234 68.980503 10.502544 3.24076 CV = 8.56 R VARIANCE AND COVARIANCE COMPONENT ESTIMATES FROM DIRECT ANALYSIS K FOR RANDOM EFFECTS COMPONENT (SIRE) = 103.2884 DEGREES OF FREEDOM = 9. SS, CP, MS, MCP, VARIANCE AND COVARIANCE COMPONENTS JOB ROW COL RHM COMPONENTS 1 1 1 EGGNUM 6.95764196 1 1 2 EGGNUM 51.66094724 1 1 3 EGGNUM 1.69474427 1 1 1 RHM SS OR CP MS OR COV EGGNUM 7915.46662447 879.49629161 BWFE -49560.95053938 -5506.77228215 - EGGWT -1649.37941216 -183.26437913 - 2 2 BWFE 698.51732702 2 3 BWFE 11.93077794 BWFE 724300.11947014 80477.79105224 EGGWT 11692.06764008 1299.11862668 3 3 EGGWT .56616207 EGGWT 620.82452638 68.98050293 APPENDIX 7 ANALYSIS OF VARIANCE FOR SELECTION CRITERIA TRAITS IN G2 (WHOLE POPULATION) RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR -.1152 1 2 1 2 1 3 2 BWFE 1.0000 3 BWFE .2494 BWFE 5831125.799820 10141.08834751 EGGWT 46081.077095 80.14100364 3 EGGWT 1.0000 EGGWT 5856.239919 10.18476508 LEAST-SQUARES ANALYSIS OF VARIANCE EGGNUM D.F. SUM OF SQUARES MEAN SQUARES F SOURCE PROB TOTAL 585 89101.459829 TOTAL REDUCTION 10 5252.593250 3.602 .0001 MU-YM 1 14.184874 .097 .7552 SIRE 9 5252.593250 4.002 .0001 REMAINDER 575 83848.866579 MEAN = 44.35214 ERROR STANDARD DEVIATION = SQUARED = .059 R = .243 525.259325 14.184874 583.621472 145.824116 12.07577 CV = 27.23 R BWFE SOURCE D.F. SUM OF SQUARES MEAN SQUARES F PROB TOTAL 585 6462219.316239 TOTAL REDUCTION 10 631093.516420 63109.351642 6.223 .0000 MU-YM 1 136.248446 136.248446 .013 .9078 SIRE 9 631093.516420 70121.501824 6.915 .0000 REMAINDER 575 5831125.799820 10141.088348 MEAN = 953.07419 ERROR STANDARD DEVIATION = 100.70297 CV = 10.57 R SQUARED = .098 R = .313 EGGWT SOURCE D.F. SUM OF SQUARES MEAN SQUARES F PROB TOTAL 585 6313.572650 TOTAL REDUCTION 10 457.332731 45.733273 4.490 .0000 MU-YM 1 1.648401 1.648401 .162 .6876 SIRE 9 457.332731 50.814748 VARIANCE AND COVARIANCE COMPONENT ESTIMATES FROM DIRECT ANALYSIS K FOR RANDOM EFFECTS COMPONENT (SIRE)= 58.3271 DEGREES OF FREEDOM = 9. SS, CP, MS, MCP, VARIANCE AND COVARIANCE COMPONENTS JOB ROW COL RHM COMPONENTS 1 1 1 EGGNUM 7.50590403 1 1 2 EGGNUM 29.61541521 1 1 3 EGGNUM .11719491 1 1 1 RHM SS OR CP MS OR EGGNUM 5252.59324988 583.62147221 BWFE -16125.09802925 -1791.67755881 EGGWT 21.57591848 2.39732428 2 2 BWFE 1028.34615345 2 3 BWFE 2.74202036 BWFE 631093.51641958 70121.50182440 EGGWT 2160.67504142 240.07500460 3 3 EGGWT .69658884 EGGWT 457.33273060 50.81474784 COV - APPENDIX 8 ANALYSIS OF VARIANCE FOR SELECTION CRITERIA TRAITS IN G0 (SELECTED POPULATION) RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR COV CORRELATION 1 1 1 EGGNUM EGGNUM 74157.346599 211.87813314 1.0000 1 1 2 EGGNUM BWFE -141304.505235 -403.72715781 -.2444 1 1 3 EGGNUM EGGWT -4030.533933 -11.51581124 -.2108 1 3 3 EGGWT 1.0000 EGGWT 4930.631848 14.08751957 LEAST-SQUARES ANALYSIS OF VARIANCE EGGNUM D.F. SUM OF SQUARES MEAN SQUARES F SOURCE PROB TOTAL 360 81150.330556 TOTAL REDUCTION 10 6992.983957 3.300 .0004 MU-YM 1 13.199070 .062 .8031 SIRE 9 6992.983957 3.667 .0002 REMAINDER 350 74157.346599 MEAN = 34.13611 ERROR STANDARD DEVIATION = SQUARED = .086 R = .294 699.298396 13.199070 776.998217 211.878133 14.55603 CV = 42.64 R BWFE SOURCE D.F. SUM OF SQUARES MEAN SQUARES F PROB TOTAL 360 5293262.222222 TOTAL REDUCTION 10 786412.997243 78641.299724 6.107 .0000 MU-YM 1 5075.561962 5075.561962 .394 .5305 SIRE 9 786412.997243 87379.221916 6.786 .0000 REMAINDER 350 4506849.224979 12876.712071 MEAN = 962.50222 ERROR STANDARD DEVIATION = 113.47560 CV = 11.84 R SQUARED = .149 R = .385 EGGWT SUM OF SQUARES SOURCE D.F. PROB TOTAL 360 5615.988889 TOTAL REDUCTION 10 685.357041 4.865 .0000 MU-YM 1 15.180512 1.078 .3000 SIRE 9 685.357041 5.406 .0000 REMAINDER 350 4930.631848 MEAN = 36.50556 ERROR STANDARD DEVIATION = MEAN SQUARES F 68.535704 15.180512 76.150782 14.087520 3.75333 CV = 10.28 R FREEDOM = 9. SS, CP, MS, MCP, VARIANCE AND COVARIANCE COMPONENTS JOB ROW COL RHM COV COMPONENTS 1 1 1 EGGNUM 776.99821739 15.78248378 1 1 2 EGGNUM 1582.04262819 -32.90759843 1 1 3 EGGNUM 84.80425440 -2.04677501 1 1 1 RHM SS OR CP EGGNUM 6992.98395650 BWFE -14238.38365370 - EGGWT -763.23828961 - 2 2 BWFE BWFE 87379.22191591 2080.68105484 2 3 BWFE EGGWT 2098.10157534 51.94584664 3 3 EGGWT EGGWT 76.15078232 1.73328194 MS OR 786412.99724321 18882.91417802 685.35704086 APPENDIX 9 ANALYSIS OF VARIANCE FOR SELECTION CRITERIA TRAITS IN G1 (SELECTED POPULATION) RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR COV CORRELATION 1 1 1 EGGNUM EGGNUM 60270.518139 120.54103628 1.0000 1 1 2 EGGNUM BWFE -91390.829933 -182.78165987 -.2083 1 1 3 EGGNUM EGGWT 227.525205 .45505041 .0143 1 2 1 2 2 BWFE 1.0000 3 BWFE BWFE 3192909.231453 6385.81846291 EGGWT 29620.212677 59.24042535 LEAST-SQUARES ANALYSIS OF VARIANCE EGGNUM D.F. SUM OF SQUARES MEAN SQUARES F SOURCE PROB TOTAL 510 65264.198039 TOTAL REDUCTION 10 4993.679901 499.367990 4.143 .0000 MU-YM 1 25.962830 25.962830 .215 .6428 SIRE 9 4993.679901 554.853322 4.603 .0000 REMAINDER 500 60270.518139 120.541036 MEAN = 43.20196 ERROR STANDARD DEVIATION = 10.97912 25.41 R SQUARED = .077 R = .277 BWFE SOURCE D.F. SUM OF SQUARES PROB TOTAL 510 3591086.470590 TOTAL REDUCTION 10 398177.239137 6.235 .0000 MU-YM 1 454.346601 .071 .7898 SIRE 9 398177.239135 6.928 .0000 REMAINDER 500 3192909.231453 MEAN = 1024.64706 ERROR STANDARD DEVIATION = SQUARED = .111 R = .333 EGGWT SOURCE D.F. SUM OF SQUARES PROB TOTAL 510 4641.992157 TOTAL REDUCTION 10 451.550494 5.388 .0000 MU-YM 1 3.058637 .365 .5460 SIRE 9 451.550494 5.987 .0000 REMAINDER 500 4190.441663 MEAN = 38.06275 ERROR STANDARD DEVIATION = SQUARED = .097 R = .312 CV = MEAN SQUARES F 39817.723914 454.346601 44241.915459 6385.818463 79.91132, CV = 7.80 R MEAN SQUARES F 45.155049 3.058637 50.172277 8.380883 2.89498 CV = 7.61 R JOB ROW COL RHM COMPONENTS 1 1 1 EGGNUM 8.53693309 1 1 2 EGGNUM -2.88898702 1 1 3 EGGNUM -2.40479173 1 1 1 RHM SS OR CP MS EGGNUM 4993.67990052 554.85332228 BWFE -2967.81712554 -329.75745839 EGGWT -1096.98795032 -121.88755004 2 2 BWFE 744.10735636 2 3 BWFE -1.72992633 BWFE 398177.23913504 44241.91545945 EGGWT -258.91855931 -28.76872881 3 3 .82146037 EGGWT 451.55049360 50.17227707 EGGWT OR COV APPENDIX 10 ANALYSIS OF VARIANCE FOR SELECTION CRITERIA TRAITS IN G2 (SELECTED POPULATION) RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR COV CORRELATION 1 1 1 EGGNUM EGGNUM 38029.893237 138.29052086 1.0000 1 1 2 EGGNUM BWFE -12882.540965 -46.84560351 -.0481 1 1 3 EGGNUM EGGWT -515.935366 -1.87612860 -.0522 1 2 1 2 2 BWFE 1.0000 3 BWFE .4317 BWFE 1889095.747730 6869.43908266 EGGWT 30079.113280 109.37859374 PROB TOTAL 285 41090.842105 TOTAL REDUCTION 10 3060.948869 2.213 .0173 MU-YM 1 5.579168 .040 .8410 SIRE 9 3060.948869 2.459 .0105 REMAINDER 275 38029.893237 MEAN = 47.17526 ERROR STANDARD DEVIATION = SQUARED = .074 R = .273 BWFE SUM OF SQUARES SOURCE D.F. PROB TOTAL 285 2151386.842125 TOTAL REDUCTION 10 262291.094395 3.818 .0001 MU-YM 1 45.744720 .007 .9350 SIRE 9 262291.094375 4.242 .0000 REMAINDER 275 1889095.747730 MEAN = 1062.90526 ERROR STANDARD DEVIATION = SQUARED = .122 R = .349 EGGWT SOURCE D.F. SUM OF SQUARES PROB TOTAL 285 2849.663158 TOTAL REDUCTION 10 279.941032 2.996 .0013 MU-YM 1 .010331 .001 .9735 SIRE 9 279.941032 3.329 .0008 REMAINDER 275 2569.722126 MEAN = 38.64316 ERROR STANDARD DEVIATION = SQUARED = .098 R = .313 306.094887 5.579168 340.105430 138.290521 11.75970 CV = 24.96 R MEAN SQUARES F 26229.109439 45.744720 29143.454931 6869.439083 82.88208 CV = 8.03 R MEAN SQUARES F 27.994103 .010331 31.104559 9.344444 3.05687 CV = 8.12 R VARIANCE AND COVARIANCE COMPONENT ESTIMATES FROM DIRECT ANALYSIS K FOR RANDOM EFFECTS COMPONENT (SIRE) = 28.3376 DEGREES OF 1 1 1 1 1 7.12180119 1 2 EGGNUM BWFE 1.45073533 1 3 EGGNUM EGGWT .77606209 -51.61692970 -5.73521441 181.04062952 20.11562550 2 2 BWFE 786.02276435 2 3 BWFE 3.27630661 BWFE 262291.09437481 29143.45493053 EGGWT 1819.99198331 202.22133148 3 3 EGGWT .76788783 EGGWT 279.94103227 31.10455914 APPENDIX 11 ANALYSIS OF VARIANCE FOR SELECTION CRITERIA TRAITS IN G0 (CONTROL POPULATION) RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR COV CORRELATION 1 1 1 EGGNUM EGGNUM 49993.748047 80.76534418 1.0000 1 1 2 EGGNUM BWFE -115586.058164 -186.73030398 -.2564 1 1 3 EGGNUM EGGWT -2897.607533 -4.68111072 -.1692 1 2 1 2 1 3 2 BWFE 1.0000 3 BWFE .4542 BWFE 4063616.677602 6564.80884911 EGGWT 70114.932702 113.27129677 3 EGGWT 1.0000 EGGWT 5863.135740 9.47194788 LEAST-SQUARES ANALYSIS OF VARIANCE MU-YM 1 .628421 .008 .9297 SIRE 9 3075.953065 4.232 .0000 REMAINDER 619 49993.748047 MEAN = 34.04173 ERROR STANDARD DEVIATION = SQUARED = .058 R = .241 BWFE SUM OF SQUARES SOURCE D.F. PROB TOTAL 629 4580540.540541 TOTAL REDUCTION 10 516923.862939 7.874 .0000 MU-YM 1 844.090560 .129 .7200 SIRE 9 516923.862938 8.749 .0000 REMAINDER 619 4063616.677602 MEAN = 880.13703 ERROR STANDARD DEVIATION = SQUARED = .113 R = .336 EGGWT SOURCE D.F. SUM OF SQUARES PROB TOTAL 629 6301.570747 TOTAL REDUCTION 10 438.435007 4.629 .0000 MU-YM 1 .013317 .001 .9701 SIRE 9 438.435007 5.143 .0000 REMAINDER 619 5863.135740 MEAN = 35.26951 ERROR STANDARD DEVIATION = SQUARED = .070 R = .264 .628421 341.772563 80.765344 8.98695 CV = 29.07 R MEAN SQUARES F 51692.386294 844.090560 57435.984771 6564.808849 81.02351 CV = 11.07 R MEAN SQUARES F 43.843501 .013317 48.715001 9.471948 3.07765 CV = 8.70 R VARIANCE AND COVARIANCE COMPONENT ESTIMATES FROM DIRECT ANALYSIS K FOR RANDOM EFFECTS COMPONENT (SIRE) = 62.7278 DEGREES OF FREEDOM = 9. SS, CP, MS, MCP, VARIANCE AND COVARIANCE COMPONENTS JOB ROW COL RHM RHM SS OR CP MS OR COV .11866423 1 1 1 2 2 BWFE 810.98311723 2 3 BWFE 11.26384396 BWFE 516923.86293841 57435.98477093 EGGWT 7378.44567646 819.82729738 3 3 EGGWT .62560876 EGGWT 438.43500688 48.71500076 APPENDIX 12 ANALYSIS OF VARIANCE FOR SELECTION CRITERIA TRAITS IN G1 (CONTROL POPULATION) RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR COV CORRELATION 1 1 1 EGGNUM EGGNUM 56385.651390 116.25907503 1.0000 1 1 2 EGGNUM BWFE -62340.143486 -128.53637832 -.1477 1 1 3 EGGNUM EGGWT -5128.541168 -10.57431169 -.2976 1 2 1 2 1 3 SOURCE PROB TOTAL 2 BWFE 1.0000 3 BWFE .2739 BWFE 3161449.569819 6518.45272128 EGGWT 35339.464033 72.86487429 5266.266533 10.85828151 3 EGGWT EGGWT 1.0000 LEAST-SQUARES ANALYSIS OF VARIANCE EGGNUM D.F. SUM OF SQUARES MEAN SQUARES F 495 59942.391919 3.399 .0005 REMAINDER 485 56385.651390 MEAN = 37.04040 ERROR STANDARD DEVIATION = SQUARED = .059 R = .244 BWFE SUM OF SQUARES SOURCE D.F. PROB TOTAL 495 3482173.725253 TOTAL REDUCTION 10 320724.155434 4.920 .0000 MU-YM 1 .981104 .000 .9902 SIRE 9 320724.155434 5.467 .0000 REMAINDER 485 3161449.569819 MEAN = 831.19071 ERROR STANDARD DEVIATION = SQUARED = .092 R = .303 EGGWT SUM OF SQUARES SOURCE D.F. F PROB TOTAL 495 5701.931313 TOTAL REDUCTION 10 435.664781 .0000 MU-YM 1 1.135979 .7465 SIRE 9 435.664781 .0000 REMAINDER 485 5266.266533 MEAN = 37.38365 ERROR STANDARD DEVIATION = SQUARED = .076 R = .276 116.259075 10.78235 CV = 29.75 R MEAN SQUARES F 32072.415543 .981104 35636.017270 6518.452721 80.73694 CV = 9.20 R MEAN SQUARES 43.566478 4.012 1.135979 .105 48.407198 4.458 10.858282 3.29519 CV = 9.19 R VARIANCE AND COVARIANCE COMPONENT ESTIMATES FROM DIRECT ANALYSIS K FOR RANDOM EFFECTS COMPONENT (SIRE) = 49.4272 DEGREES OF FREEDOM = 9. SS, CP, MS, MCP, VARIANCE AND COVARIANCE COMPONENTS JOB ROW COL RHM RHM COMPONENTS 1 1 1 EGGNUM EGGNUM 5.64334091 SS OR CP MS OR 3556.74052872 395.19339208 COV 1 1 1 2 2 BWFE 589.10049168 2 3 BWFE 7.05615078 3 3 EGGWT .75968184 BWFE 320724.15543382 35636.01727042 EGGWT 3794.67334097 421.63037122 EGGWT 435.66478057 48.40719784 APPENDIX 13 ANALYSIS OF VARIANCE FOR SELECTION CRITERIA TRAITS IN G2 (CONTROL POPULATION) RESIDUAL MATRICES FOR RIGHT HAND MEMBERS JOB ROW COL RHM RHM ERROR SS OR CP ERROR MS OR COV CORRELATION 1 1 1 EGGNUM EGGNUM 46642.131188 170.22675616 1.0000 1 1 2 EGGNUM BWFE -59092.145926 -215.66476615 -.2270 1 1 3 EGGNUM EGGWT -5762.462509 -21.03088507 -.4588 1 1 1 2 2 1.0000 2 3 .1751 BWFE BWFE 1452593.545394 5301.43629706 BWFE EGGWT 12269.338718 44.77860846 3 3 1.0000 EGGWT EGGWT 3381.549343 12.34142096 LEAST-SQUARES ANALYSIS OF VARIANCE EGGNUM D.F. SUM OF SQUARES MEAN SQUARES F SOURCE PROB TOTAL TOTAL REDUCTION 2.226 .0167 284 10 50430.718310 3788.587122 378.858712 MEAN = 38.73944 ERROR STANDARD DEVIATION = SQUARED = .075 R = .274 BWFE SUM OF SQUARES SOURCE D.F. PROB TOTAL 284 1654110.207747 TOTAL REDUCTION 10 201516.662353 3.801 .0001 MU-YM 1 .000614 .000 .9997 SIRE 9 201516.662353 4.224 .0000 REMAINDER 274 1452593.545394 MEAN = 859.94718 ERROR STANDARD DEVIATION = SQUARED = .122 R = .349 EGGWT SOURCE D.F. SUM OF SQUARES PROB TOTAL 284 3765.887324 TOTAL REDUCTION 10 384.337981 3.114 .0009 MU-YM 1 2.843119 .230 .6316 SIRE 9 384.337981 3.460 .0005 REMAINDER 274 3381.549343 MEAN = 35.16901 ERROR STANDARD DEVIATION = SQUARED = .102 R = .319 13.04710 CV = 33.68 R MEAN SQUARES F 20151.666235 .000614 22390.740261 5301.436297 72.81096 CV = 8.47 R MEAN SQUARES F 38.433798 2.843119 42.704220 12.341421 3.51304 CV = 9.99 R VARIANCE AND COVARIANCE COMPONENT ESTIMATES FROM DIRECT ANALYSIS K FOR RANDOM EFFECTS COMPONENT (SIRE) = 28.2926 DEGREES OF FREEDOM = 9. SS, CP, MS, MCP, VARIANCE AND COVARIANCE COMPONENTS JOB ROW COL RHM RHM COMPONENTS 1 1 1 EGGNUM EGGNUM 8.86192757 1 1 2 EGGNUM BWFE 44.28626327 SS OR CP MS OR COV 3788.58712193 420.95412466 -13217.76252505 -1468.64028056 - .62124123 1 3 3 EGGWT 1.07316935 EGGWT 384.33798128 42.70422014 90 92