ABSTRACT
This study was conducted to evaluate egg production curves of Shikabrown® parents, using mathematical models. A total of 200 birds: 100from each of the two strains of Shikabrown® parents (sire and dam) lines at the Breeding Unit of Poultry Research Programme, National Animal Production Research Institute (NAPRI) were used for the study. The birds were obtained from the selected lines (sire and dam) and were denoted as strain A and strain B, respectively. Body weight (BWT), age at sexual maturity (ASM), egg number (EGGNO), and egg weight (EWT) were examined. Four non-linear models (Logistic, Richard, Gompertz, and Exponential) and a linear model were used to predict the efficiency of weekly bodyweight and egg production traits. Genetic parameters (heritability, genotypic and phenotypic) correlations were estimated for egg production. Genetic parameters were estimated using VARCOMP procedure of SAS. The adequacies of the models were fitted using R Package, version 3.0.3.High coefficients of determination for BWT (R2 = 0.84 – 0.93) were recorded in the models for both strains. Strain A had higher R2 (0.93) for BWT in Richard, Gompertz and Exponential models while strain B recorded (R2 = 0.89) in Logistic, Richard and Gomprtz models. High coefficient of determination was obtained in a reproductive trait; egg number; in which almost all the models gave (R2 = 0.70). Exponential model recorded a higher R2 (0.93) for EGGNO in strain A. This suggests that the strains had similar age at sexual maturity and it implies that the birds‟ genetic potential can be further exploited for more genetic improvement. EWT in strain A recorded higher R2 (0.96) coefficient of determination across the four nonlinear models except linear model with (R2 = 0.95) for egg weight. Significant (P<0.05) differences were recorded within models for the egg production traits studied. Significant differences (P<0.05) were observed in the birds‟ performance for BWT and EWT, with strain B having a higher BWT (1.59±0.01) and strain A having a higher EWT (48.75±0.17). Similarly, age of
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birds in lay had a concomitant significant differences (P<0.05) in their BWT as well as their EWT. The birds performed better for BWT and EWT in week 26 and 27 for both strains. Strain B had higher heritability estimates (h2=0.45) while the least estimates (h2=0.10) was recorded in strain A for EGGNO. ASM recorded the highest estimates (h2=0.48) in strain A while least value (h2=0.18)was observed in strain B. BWT had high genotypic correlations with EWT (rg= 0.88) and ASM (rg= 0.48) in strain A. EGGNO had low genotypic correlation with BWT (rg= 0.01). EWT had negative and low genotypic correlation with EGGNO (rg= -0.05).ASM was negatively correlated with EGGNO (rg= -0.93) and EWT (rg= -0.05). It was concluded that strain significantly (P<0.05) had effect on BWT and EWT of Shikabrown® parent with stain B performing better than strain A in BWT and strain A better than strain B in EWT. Coefficient of determination (R2) obtained from Richard; MLR and Gompertz models can be used to estimate egg number, body and egg weights. R2 identified differences between strains in predicting egg production traits. Strain B was adjudged good and profitable because the strain had the highest mean values in body weight and egg number and it is being recommended as one of the lines for future improvement of Shikabrown®. Egg weights of Shikabrown® should be improved based on the recorded genetic variability in the parents.
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TABLE OF CONTENTS
Title Page……………………………………………………………….. ……………………. i
Declaration ……………………………………………………………………………….ii
Certification …………………..…………………………………………………………iii
Acknowledgement …………………………………………………………………………………………… iiiv
Dedication ………………………………………………………………………………………………………… iv
Table of Contents………………………………………………………………………vi
List of Figures…………………………………………………………………………vii
Abstract ………………………………………………………………………………………………………….. viii
CHAPTER ONE ………………………………………………………………………………………………. 1
1.0 INTRODUCTION ………………………………………………………………………………….. 1
1.1 Background of the Study ………………………………………………………………………….. 1
1.2 Statement of the Problem ………………………………………………………………………….. 3
1.3 Justification of the study …………………………………………………………………………… 4
1.4 Aim ……………………………………………………………………………………………………….. 4
1.5 Objectives ………………………………………………………………………………………………. 4
1.6 Hypotheses ……………………………………………………………………………………………… 5
CHAPTER TWO ……………………………………………………………………………………………… 6
2.0 LITERATURE REVIEW ……………………………………………………………………….. 6
2.1 Historical Development of Egg Type Chickens in Nigeria ……………………………. 6
2.2 Importance of Poultry Production in Nigeria ………………………………………………. 6
2.3 Factors Affecting Egg Production in Nigeria ………………………………………………. 7
2.3.1 Breed of bird …………………………………………………………………………………………… 7
2.3.2 Mortality rate ………………………………………………………………………………………….. 8
2.3.3 Age at sexual maturity (Age of birds at lay) ………………………………………………… 8
2.3.4 Body weight of birds at lay ……………………………………………………………………….. 9
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2.3.5 Site and construction of laying house ………………………………………………………… 10
2.3.6 Lighting schedules in layer house ……………………………………………………………… 10
2.3.7 Types of feed and feed utilization ability of birds ………………………………………… 11
2.3.8 Culling of unproductive birds ……………………………………………………………………. 11
2.3.9 Effect of climate………………………………………………………………………………………. 11
2.3.10 Management practices …………………………………………………………………………….. 12
2.3.11Vaccination and disease control ………………………………………………………………… 12
2.3.12Collection of eggs ……………………………………………………………………………………. 12
2.4 Modeling of Production Pattern in Livestock ……………………………………………….. 13
2.5 Effects of Model Parameters on Egg Traits and Growth Curves ……………………… 14
2.6 Different Mathematical Models Used in Laying Birds ……………………………….. 15
2.7 Derived Models for Egg Production Curve from Lactation Curve ……………….. 17
2.7.1 Gamma function ……………………………………………………………………………………. 17
2.7.2 Modification of Wood model, applied to poultry ………………………………………. 18
2.7.3 McMillan function ………………………………………………………………………………… 18
2.7.4 Algebraic function ………………………………………………………………………………… 19
2.7.5 Compartmental model……………………………………………………………………………. 19
2.7.6 Post-peak of linear regression …………………………………………………………………. 20
2.7.7 Logistic model ……………………………………………………………………………………… 20
2.7.8 Modification of Compartmental Model ……………………………………………………. 20
2.7.9 Gloor function ………………………………………………………………………………………. 21
2.7.10 McNally model …………………………………………………………………………………….. 21
2.7.11 Segmented polynomial model ………………………………………………………………… 22
2.7.12 Persistency model …………………………………………………………………………………. 22
2.8 Egg Production Curve …………………………………………………………………………. 23
2.8.1 Classification of models………………………………………………………………………….. 25
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2.9 Egg Production Traits …………………………………………………………………………. 26
2.10 Genetic Parameter (Phenotypic and Genotypic Correlations) Estimates for Egg Production Traits …………………………………………………………………………. 26
2.10.1 Heritability of egg production traits………………………………………………………… 28
2.10.2 Egg quality traits ………………………………………………………………………………….. 30
2.10.3 Egg number …………………………………………………………………………………………. 32
2.10.4 Egg weight ………………………………………………………………………………………….. 33
CHAPTER THREE ………………………………………………………………………………………… 34
3.0 MATERIALS AND METHODS ……………………………………………………………. 34
3.1 Location of the Study Area …………………………………………………………………… 34
3.2 Experimental Birds ……………………………………………………………………………….. 34
3.3 Management Practices …………………………………………………………………………. 35
3.3.1 Egg collection and hatching …………………………………………………………………….. 35
3.3.2 Management of birds ……………………………………………………………………………… 35
3.3.3 Nutrition ………………………………………………………………………………………………… 36
3.4 Production Traits Measured ………………………………………………………………….. 36
3.5 Egg Quality Traits Estimations ……………………………………………………………… 36
3.5.1 External egg quality traits measured ………………………………………………………….. 37
3.5.2 Internal egg quality traits measured …………………………………………………………… 37
3.6 Data Collection …………………………………………………………………………………….. 38
3.7 Data Analysis ……………………………………………………………………………………….. 38
3.7.1 Analysis of variance……………………………………………………………………………….. 38
3.7.2 Correlation procedure …………………………………………………………………………….. 39
3.7.3 Regression analysis for external egg quality traits …………………………………….. 39
3.7.4 Principal component analysis for egg quality traits …………………………………….. 39
3.7.5 Estimation of variance components, heritability and correlations …………………. 40
3.8 Models Fitted ……………………………………………………………………………………….. 40
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3.9 Statistical Criteria to Evaluate the Fitted Curves …………………………………… 43
3.9.1 Akaike‟s information criterion (AIC) ……………………………………………………….. 43
3.9.2 Mean square error (MSE) ……………………………………………………………………….. 43
3.9.3 Coefficient of determination (R2) …………………………………………………………….. 44
3.9.4 Model error ………………………………………………………………………………………….. 44
3.9.5 Graphical evaluation of curve fitting ……………………………………………………….. 45
4.0 RESULTS …………………………………………………………………………………………… 46
4.1 Effects of Strain and Age on Egg Production Traits of Shikabrown®Parent ………………………………………………………………………………….46
4.2 Effects of Strain on Body Weight of Shikabrown® Parent ……………………… 46
4.3 Effect of Age on Egg Weight …………………………………………………………………. 49
4.4 Variation in Egg Number of Shikabrown® Parent over Time…………………. 51
4.5 Models Comparism of Strain A and Strain B for Body Weight ……………… 53
4.6 Model Comparison for Egg Number in Strain A and Strain B ……………….. 59
4.7 Model Comparison for Egg Weight in Strain A and Strain B ………………… 64
4.8 Egg Production Traits Comparison for Strain A and Strain B ……………….. 69
4.9 Error of Prediction for Linear Regression Model in Strain A and Strain B ………………………………………………………………………………….72
4.10 Performance of Strains for Egg Quality Traits………………………………………. 80
4.11 Principal Component Analysis for Egg quality Traits in Strain A ………….. 82
4.12 Principal Component Analysis for Egg Quality Traits in Strain B …………. 83
4.13 Phenotypic Correlation among Internal and External Egg Quality Traits in Strain A ……………………………………………………………………………………………….. 86
4.14 Phenotypic Correlation among Internal and External Egg Quality Traits in Strain B ……………………………………………………………………………………………….. 88
4.15 Estimates of Heritability of Production Traits in Strain A and B …………… 90
4.16 Genotypic (rg) and Phenotypic (rp) Correlation Coefficients for Egg Production Traits in Strain A ……………………………………………………………….. 92
4.17 Genotypic (rg) and Phenotypic (rp) Correlation Coefficients for Egg Production Traits in Strain B ………………………………………………………………. 94
4.18 Genotypic Correlations (rg) for Production Traits in both Strains …………. 96
4.19 Prediction Equation of Egg Weight using External Egg Quality Traits ….. 98
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CHAPTER FIVE ……………………………………………………………………………………………. 99
5.0 DISCUSSIONS …………………………………………………………………………………….. 99
5.1 Egg Production Traits of Shikabrown® Layers …………………………………….. 99
5.2 Egg Production Performance of Shikabrown® Parent Using Models…….102
5.3 Error of Prediction by Linear Regression Model …………………………………. 104
5.4 Internal and External Egg Quality Traits in both Strains …………………….. 105
5.5 Principal Component Analysis in both Strains …………………………………….. 107
5.5.1 Principal component analysis for egg quality traits in both strain……………109
5.5.2 Phenotypic correlated matrix for egg quality traits in both strains……………111
5.6 Heritability Estimates of Egg Production Traits in both Strains ……………. 111
5.6.1 Genotypic (rg) and phenotypic (rp) correlations of egg production traits among Strains ………………………………………………………………………………………………… 112
5.6.2 Prediction equation of egg weight using external egg quality traits. ……………… 113
CHAPTER SIX …………………………………………………………………………………………….. 115
6.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS …………….. 115
6.1 Summary……………………………………………………………………………………………. 115
6.2 Conclusions ………………………………………………………………………………………… 117
6.3 Recommendations ………………………………………………………………………………. 118
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Study
The domestication of livestock species some ten thousand years ago was a vital step in the development of human civilization. Over the centuries, domestication evolved into breeding and the genetic improvement of livestock such as laying birds and broilers (Tercic and Holcman, 2008). Egg production is the single most important phenotype for evaluating the productivity of laying birds. It helps in evaluating the efficiency of management and optimum managerial practices that will sustain gain at optimum level (Aboul-Seoud, 2008).
Egg production is known to be a complex quantitative trait; it depicts a considerable variation over time within the production cycle of a hen. Several methods of expressing egg production and its component characters have been studied (Schreiweis et al., 2006; Dogan et al., 2010). Despite the application of different forms of analysis of variance, however, it remained difficult to give a clear explanation of the variation in egg production over time (Dogan et al., 2010). However, studies by Oni (1997) have shown that when egg production in chickens is summarized on a weekly, biweekly or monthly basis, it gradually increases, attained peak and persist and then gradually decline. In egg production peak is usually attained a month after first egg is laid (Savegnago et al., 2012). Although variations to this exist among breeds, strains and lines. This regularity, though not a steady process over time, is generally denoted as egg production curve in poultry. Egg production curves are useful tools representing the evolution of egg production changes and are of particular importance in both breeding and management. Values such as point of inflection, effects of different management systems, feeding requirements and the results of breeding applications can be evaluated using egg
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production curves (Narinc et al., 2010). Fairfull and Gowe (1990) reported that mathematical models can be used to forecast income and flock performance to evaluate theoretical expectations or to predict whole record performance based on part record of egg production. A mathematical model describing such a curve could enable poultry breeders and commercial egg producers to analyze egg production process as well as to predict annual production from part records (Oni, 1997; Fairfull, Gowe, 1990).
Models used to define growth process in animal science include Gompertz Koivula et al. (2007) in Finnish Yorkshire boars, gilts and barrows. Richard and Logistic (Osei-Amponsah et al., 2014; Grossman and Bohren (1985) in local chickens. Bertalanffy (1938), Barbato (1991) in chickens. Hyperbolastic models which were proposed by Tabatabai et al. (2005)have also been used in recent years, while McNally (1971), Gavora et al. (1982) and McMillan et al. (1986) studied egg production curves extensively, expressing it as a function of calendar time periods. Evaluation of egg quality is important for both egg laying and breeder flocks. Egg weight, yolk color and shell thickness are the most important quality traits of consumed egg (Stadelman, 1995). Shell thickness, breaking strength, specific gravity, albumen height, yolk height and some other quality traits are also important for hatching and consumed egg (Wolanski et al., 2007). Classification of these traits into components could be very helpful in constructing a robust selection index for poultry birds. Principal components analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to reduce a set of correlated variables into a set of uncorrelated variables called principal components. It is a means of identifying patterns in the data by their similarities and differences and a method to compress the data information without much loss of information (Hair et al., 2009).
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1.2 Statement of the Problem
The Shikabrown® commercial layers were developed as a result of many years of breeding and selection work at the National Animal Production Research Institute (NAPRI), Zaria. The birds were obtained from sets of crosses between two specialized lines of the foundation stock and were tested and proven for good performance in all the six geo-political zones of Nigeria (Kallah, 1999). A complete description of the foundation stock and breeding activities on the egg-type chickens in the Institute (NAPRI) was given by (Adeyinka 1998 and Ikani et al., 2014). A total of 1,411 day old grandparent-stock belonging to two strains of egg-type chickens, made up of male (sire), line bred for high body weight: and female (dam), line bred for high egg number were imported between February and April 1985. The sire line has a golden or brown color while the dam line has white or silvery plumage (Kabir and Muhammad, 2012). The chicken is resilient to all known diseases and is suitable for research purposes as it can lay eggs for consecutive two years (Gefu, 2014).
The egg production curve of Shikabrown® was studied about two decades ago by Oni (1997). A lot of changes might have occurred in egg production trends in response to selection over these years. This may be due to the fact that those models lacked optimization capability in the model parameters. Most recently developed models are constructed with optimization qualities and fast response in time needed for convergence. Therefore, there is a need to re-evaluate the egg production curve of Shikabrown® using these mathematical models (Gompertz, Logistic, Richard, Exponential, and multiple linear regression). Mathematical models provide one means of predictions, but they are sometimes inadequate due to poor extrapolative properties or abnormal deviations from expectations (Adams-Bell, 1980). The accuracy of predicting full record from part record production has become highly important in
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assessing the relative merits of models describing poultry egg production records. Haruna et al. (2007) reported that prediction of the whole egg production from part-period production can be maximized by a careful analysis of appropriate data.
1.3 Justification of the Study
Shikabrown® chickens had been tested and certified as a good stock of chicken in the six geo-political zones of Nigeria. However, the institutional evaluation of the production pattern of the chicken was last done in 1997. Change of climatic elements and its effect on livestock had been documented to fit broadly into one of two categories: loss of productivity and increasing cost of production (Adesiji et al., 2013; The Poultry Site, 2009). Evaluation of egg production patterns of Shikabrown® parents after the last one that was done about 20 years ago is necessary.
1.4 Aim
The aim of this study is to evaluate the egg production trends of Shikabrown® parents using mathematical models. This was also carried out to compare and evaluate the egg production curves of Shikabrown® parent using mathematical models in Zaria – Nigeria.
1.5 Objectives:
The specific objectives of this study were to:
i. Evaluate the performance of egg production traits in two genetic lines of Shikabrown® parents
ii. Determine the adequacy of five (5) mathematical models in describing egg production curve in Shikabrown® parents
iii. Evaluate egg quality traits in the two genetic lines of Shikabrown® parents.
iv. Estimate genetic parameters (heritability, genotypic and phenotypic correlations) for egg production traits of Shikabrown® parents.
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1.6 Hypotheses:
Ho: There is no difference in egg production curves and egg quality traits of Shikabrown® parents obtained through different mathematical models. HA: There is difference in egg production curves and egg quality traits of Shikabrown® parents obtained through different mathematical models.
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