Egg Production Curves Research Articles

Modeling of Some Egg Characteristics in Henna Partridges

In this study, the distributions of egg count (daily and cumulative), width, length and weight values of chukar partridges obtained in two different egg production seasons were statistically modeled. For this purpose, in modeling cumulative egg production curves; Logistic, Gompertz, Gamma, Schunute, Brody, Richard, Negative Exponential, von Bertalanffy, Cubic Piecewise and Cubic models were used, and in evaluating daily egg production curves; Gompertz, Logistic, Richard, McNally, Gamma, Cubic Piecewise, Quadratic, Quadratic Piecewise and Modified Compartmental models were used. In modeling egg width values; Gompertz, Gamma, Cubic Piecewise and Cubic models were used, in modeling length values; Logistic, Gamma, Cubic Piecewise and Cubic models were used, and in modeling weight values; McNally, Gamma, Cubic Piecewise and Cubic models were used. In all modeling studies, as comparison and evaluation criteria; Error Mean Square, Corrected Coefficient of Determination, Accuracy Factor, Deviation Factor, Durbin-Watson, Akaike Information Criterion, Corrected Akaike Information Criterion and Bayesian Information Criterion were taken into consideration. As a result of the study; Gamma was determined as the most suitable model for modeling cumulative egg yields and length values, McNally for modeling daily egg yields and weight values, and Gompertz and Gamma models for modeling egg width values.

The influence of breed, dietary energy and lysine on laying persistency and body composition of laying hens

The effect of dietary energy and lysine levels on laying persistency and body composition in brown and white hens was studied. Dietary treatments with 2 Metabolizable Energy levels (ME lay; constant or reduction over time) and 2 apparent fecal digestible Lys levels (AFD Lys; constant or reduction over time), were fed to Lohmann white or brown hens, from 17 to 75 wk of age, in a 2 × 2 × 2 factorial design. Data were subjected to mixed model analyses. The egg production curve was modeled using a non-linear regression function. White hens showed an improved laying persistency and a higher number of total eggs per hen compared to brown hens, indicated by a shorter peak production phase but a significant lower slope of decline after peak (P < 0.05). Similarly, hensfed a reduced instead of a constant ME Lay diet had a better laying persistency, indicated by a shorter peak production phase and a significant smaller slope of decline after peak (P < 0.05). This improved laying persistency was probably related to a higher ADFI and nutrient intake of hens fed the reduced ME Lay diets (P < 0.05). A segmented regression analysis showed reasonable correlation between egg mass production and ME Lay intake, with an R2 adjusted of 0.78 for the overall model. The egg mass production was not increased for intakes above 330 kcal of ME Lay intake for white hens and 324 kcal for brown hens. Limited effects of dietary treatments on body crude fat and crude protein were found. Both energy and protein requirement seemed to increase over time, indicated by higher voluntary nutrient intake and a reducing body crude fat after wk 43 in all treatments. The egg mass production correlated poorly with the AFD Lys intake, with an R2 of 0.22. In conclusion, laying persistency was mostly influenced by breed and dietary ME Lay level, but not dietary AFD Lys level.

Investigation of egg production curve in ostrich using nonlinear functions

In most countries, ostrich farming is considered a developing branch of the efficient poultry industry. The profitability of ostrich farm requires specific consideration of productions features such as the female fertility, egg production, hatchability, and growth performance. Hence, this study aimed to fit nonlinear functions to describe the ostrich egg production pattern to achieve the most appropriate and recommendable mathematical function for future studies. For this purpose, 14,507 daily records of 184 female ostriches in 5 production seasons (periods) during 2016 to 2021 were used. Five nonlinear functions including Incomplete gamma (Wood function), Corrected gamma (McNally), nonlinear Logistic (Yang), Logistic (Nelder), and Lokhorst were fitted for modeling the egg production curve in ostrich. The goodness of fit criteria's including Mean Square Error (MSE), Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and selection of the best function. The results indicated that the Wood and the McNally functions with a slight difference in all fitting criteria were the best-fitted functions and the Yang function with the highest values of MSE, LRT, AIC, BIC, were the most inappropriate function to describe the ostrich egg production curve. The McNally and the Wood can be recommended as appropriate functions to describe egg production during 5 production seasons in the studied ostrich flock.

Individual egg production of Hy-Line Brown hens during the early laying phase in response to dietary CP levels

The aim of this study was to investigate the effects of gradually increased dietary CP level on the dynamics of individual egg production of laying hens from 18 to 54 weeks of age. Forty Hy-Line Brown hens were divided into five treatments of eight replicates each and were fed diets formulated to have five different CP concentrations: 14, 15, 16, 17, and 18% CP with the same energy content. For each experimental diet, the contents of lysine, methionine + cysteine, threonine, and tryptophan were maintained at minimum requirement concentrations by supplying synthetic amino acids. Clutch characteristics were recorded (e.g., number, size, patterns, egg weight, etc.) and individual egg production curves were modeled according to Grossman and Koops (2001). Clutch size was defined as the unstopped length of individual egg production on a daily basis. BWs were also recorded every week to account for changes in BW during the experimental period. Three hens were excluded from the statistical analyses as their laying patterns were very inconsistent. Four clutch patterns with data weighted on a weekly basis were identified. Heavier hens tended to lay more regular clutch patterns when compared to their lighter counterparts within the same treatment. In contrast, irregular clutch patterns (too many pauses between clutches) were more common in hens fed low CP diets (≤16%). This could be explained by imbalances in amino acids supply according to their theoretical requirements and adverse effects caused by amino acids interactions. Low CP diets were able to maintain egg production and mean clutch size up to 54 weeks of age. The maximum clutch size and the modeled proportion of maximum production for the increasing phase (k1) displayed a linear response to the graded addition of CP content to the diet. No differences were detected among treatments for the age at the beginning of the maximum clutch (22.6 ± 2.82 weeks). Egg weight displayed a quadratic response that was maximized when hens were fed 17% CP in the diet. Our approach appears to be promising for ranking individual laying hens based on their clutch performance to evaluate both CP and amino acids adequacy of diets.

Flaxseed and Agnus-castuson vitex as a source of phytoestrogens and their impact on productive performance, some blood constituents, and blood oestradiol profile of aged laying hens

To investigate the effect of Flaxseeds (Flax) or vitex Agnus-castuson (Vitex), a total of 180 aged hens (58 weeks of age) were divided into 5 treatment groups with 36 each as followe: groups 2 and 3 were fed the basal diet supplemented with 1 or 2 g Flax/kg, respectively. And group 4 and 5 were fed the basal diet supplemented with a 2.5 or 5 g Vitex/kg, respectively. While group 1 was served as a control group. The experiment period was lasted for two months. Flax or Vitex in hens’ rations resulted in a significant (p ≤ .001) increase in EP and EM percentages compared to the control group, as well, EW was increased significantly with the high level of Vitex only. Although, Flax or vitex did not affect daily FI, a FCR values showed a significant improvement. The high level of both Flax and Vitex boosted (p ≤ .001) the egg-shell weight than the control group. Flax or Vitex treatment resulted in a slightly decline egg albumin weight (%),while, the egg yolk weight was increased significantly(p ≤ .001). There were a significant reduction (p ≤ .001) in blood total cholesterol and LDL cholesterol concentrations in Flax or Vitex groups, Contrariwise, HDL cholesterol was a significantly increased (p ≤ .001). Blood total antioxidant capacity concentration was a significant increase (p ≤ .001) of Flax and Vitex groups than the control. Adding Flax or Vitex to hens ration boosted blood oestradiol-17β hormone secretion (p ≤ .001) than the control group. Highlights Egyptian chicken breeds are characterised by lower egg production rates compared to commercial breeds, and they reach the end of the egg production curve early. So, our aim was to use some plant materials as a phytoestrogens source to improve the reproductive and productive performance of these strains and prolong the period of egg production for them.

Mathematical modeling for egg production and egg weight curves in a synthetic white leghorn

This study was conducted to evaluate 8 mathematical models, namely, Logistic (LM), Morgqan Mercer Flodin (MMF), Polynomial Fit (PF), Rational Function (RF), Sinusoidal Fit (SF), Quadratic fit (QF), Gompertz function (GF), and Modification Compartmental Model (MCM) fitted to weekly egg production and egg weight of synthetic White Leghorn (SWL) population 21 to 40 wk of age of 5 generations (2015-16 to 2019-20). The relevant data for the present investigation were collected from SWL population, maintained in the department of Animal Genetics and Breeding, LUVAS, Hisar (India). The efficiency or reliability of the models were obtained by various criteria of goodness of fit such as coefficients of determination (R2), Root Mean Square Error (RMSE), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), graphical analysis, and Chi-square test. The results indicated that RF, MCM, SF, and PF were best models for fitting weekly egg production curve due to higher values of R2 and low values of RMSE, AIC, and BIC as compare to remaining models. In case of weekly egg weight, the best values of goodness of fit criteria were showed by MMF model followed by MCM and LM model. The results indicated that these models could be conveniently used for fitting for weekly egg production and egg weight in synthetic white leghorn, respectively.

Mathematical modeling of egg production curve in a multivariety endangered hen breed

This study aimed to compare the egg laying performance of the four varieties (white, franciscan, black, and partridge) of a Spanish endangered Utrerana hen breed. A flock of 60 Utrerana hens (15 hen/variety) were individually housed to enable daily egg traceability. Compartmental, Gamma, linear hyperbolic, logistic curvilinear, McNally, Narushin-Takma, and quadratic logarithmic nonlinear regression functions were fitted. Goodness-of-fit (coefficient of determination (R2)) and flexibility criteria (mean squared error (MSE), Akaike information criteria (AIC), corrected Akaike information criteria (AICc), Bayesian information criteria (BIC)) were evaluated to identify the best-fitting function to model for egg production curve. Best-fitting values were reported by the six-parameter Narushin-Takma model for white (R2 = 0.828), franciscan (R2 = 0.888), and black (R2 = 0.899) varieties. By contrast, quadratic logarithmic was reported to be the best-fitting model for partridge Utrerana hen egg production curve (R2 = 0.917). The characterization of the laying cycle of endangered avian breeds varieties permits tailoring productive strategies which may ensure animal welfare at the same that they boost economic potentialities, enabling the productive model to better simultaneously fulfill animal needs and human demands. As a result, breed diversity may act as the motor element to improve economic profitability, but in turn may also ensure the conservation of the local genetic resources from which desirable products, such as the egg, are obtained.

Fitness of logistics model for predicting quails egg production reared in a tropical environment with dietary betaine supplementation

Betaine is a methyl group donor and organic osmolyte, optimizing quail’s performance, particularly in a tropical environment. This study determined the fitness of the logistic model to predict the quail egg production with dietary betaine supplementation. Two hundred and four quails were divided into two dietary treatments, and six replicates with 17 quails each. The treatment diets were control (CTR) and 0.12% betaine supplementation (BET). Egg production data were collected for eleven weeks, and a T-test was performed. Next, the data is plotted to get the actual egg production curve. The fit of the logistic model is calculated according to the coefficient of determination (R2). Quail that received betaine supplementation produced more eggs than control (P<0.05). The actual egg production curve shows the effect of betaine supplementation seen after the fourth week. The logistic model predicts CTR to reach peak production faster than BET but to have lower peak production than BET (56.63% vs. 63.56%). Prediction of egg production both CTR and BET showed high accuracy with a relatively high R2 (0.88; CTR and 0.87; BET). Thus, the logistic model accurately predicted quails egg production reared in a tropical environment with betaine supplementation.

Non-linear prediction model for egg production of quails the tropics with methionine supplementation

This study aimed to predict the egg production of quails receiving methionine supplementation. Two hundred and four quails were divided into two treatment diets, and six replicates with 17 quails each. The treatment diets were control (P0) and 0.12% methionine supplementation (P1). Egg production data were collected for eleven weeks, and a T-test was performed. Next, the data was plotted to get the actual egg production curve. We used a logistic regression model to predict the egg production pattern and calculated the model’s fitness with the coefficient of determination (R2). The results showed that methionine supplementation increased egg production by 9.43% (p<0.01). Based on the actual production curve, the increase in initial production to peak production of P1 was slower than P0, but P1 had a higher egg production than P0. The logistic model predicts that peak production of P1 was higher than P0 (62.74% vs. 56.79%), although the production rate of P1 was lower than P0 (0.21 vs. 0.36). In addition, the accuracy of both P0 and P1 models was 0.88 and 0.92, respectively. Thus, the logistic model can predict quail egg production in the tropics due to diet modification with high accuracy.

Employing logistic model to predict the egg production of quails receiving digestibility enhancer supplementation

This study aimed to predict the egg production of quails supplemented with a combination of betaine and silica+. Three hundred and fifty laying quails were divided into two dietary treatments and seven replicates of 25 quails. The treatments consisted of control (T0), and supplementation with digestibility enhancer mixture consisted of 0.12% betaine and 200 ppm silica+ (T1). Egg production data were collected for three periods of 28 days, and they were subjected to T-test. The data were plotted to obtain the egg production curve. A logistic regression model was employed to identify the trend of the egg production pattern. The T1 groups produced more eggs than T0 groups (P<0.05), which attributed to the higher egg production during period II and III (P<0.01). The egg production curves indicated that production rose sharply soon after the beginning of lay. The T1 groups had a higher peak production than T0 (76.43% vs 69.20%). Furthermore, production rate was 0.17 and 0.18 and prediction accuracy was 97% and 98% for T0 and T1, respectively. Thus, the logistic model proved to be employed to analyse the biological impact of egg production in quails with a high prediction accuracy.

Mathematical Modelling of Egg Production Curves of Shikabrown Parents

This study was conducted to evaluate egg production curves of Shikabrown parents, using mathematical models. A total of 200 birds: 100 from 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. 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. 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 differences (P&lt;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 birds in lay had a concomitant significant differences (P&lt;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. It was concluded that Strain significantly (P&lt;0.05) had effect on BWT and EWT of Shikabrown® parent with Stain B performing better than Strain A in the former trait and strain A better than strain B in the latter trait. 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® .

Ovarian transcriptomic analysis and follicular development of Leizhou black duck

This study investigated the factors that caused the differences in egg production during the development of ovarian follicles in Leizhou black ducks. Leizhou black ducks population was divided into 2 groups as high-yield group (HG) and low-yield group (LG). The number of eggs (NE), age at first egg (AFE), weight at first egg, and egg weight (EW) of both groups were recorded, and differences were analyzed using the t test. The logistic model was used to simulate the egg production curves to analyze the production rules. The ovarian follicles of both duck groups were collected to count the number of different grades sized follicles, weigh the ovaries, and observe follicular sections to analyze the developmental differences. Ovarian transcriptomic sequencing was performed to investigate differentially expressed genes and signal pathways in both duck groups. The results revealed a significant difference (P < 0.01) in the NE laid, AFE, and EW between both groups. Comparatively, HG had significantly more (P < 0.01) large yellow follicles (LYF) than LG. The density of medullary layer cells of the follicle section was greater in HG than LG ducks. Transcriptome sequencing revealed a total of 1,027 differentially expressed genes between the HG and LG ducks of which 495 genes were upregulated, and 532 genes were downregulated. Fifty genes were related to reproduction and reproductive processes. Kyoto Encyclopedia of Genes and Genomes–enriched signaling pathways revealed 274 signal pathways enriched in these differentially expressed genes of which the steroid biosynthesis pathway was significantly enriched. Analysis (Q < 0.05) showed that HSD3β → gonadotropin-releasing hormone (GnRH) and estrogen receptor (ESR) → LHβ/ERK1/2 were enriched in the steroid biosynthesis signal pathway. Follicle-stimulating hormone signal pathway mediated by HSD3β → GnRH and ESR → LHβ/ERK1/2 may be involved in ovarian follicle development to regulate LYF reserve process and affect its ovulation cycle, which in turn influence the egg production of Leizhou black ducks.

Fuzzy prediction and pattern analysis of poultry egg production

Egg production is geared towards large scale production of eggs for profit maximization and consumption. But poultry farmers are faced with challenges that affect the quantity and quality of egg production during the production life cycle such as quality of feed intake, management issues, genetic factors of the breed birds, age at egg laying period, presence of diseases, molting flock, housing and others. Several mechanical solutions have been suggested to implement the production cycle but with very high cost implication which calls for the need of soft computing methods to model the egg production process. However, modelling egg production curve is a complex task because a typical egg production curve (from week 20 to week 72) is characterized by nonlinear probabilities and imprecise knowledge in the egg production process. This paper proposes an optimal fuzzy predictive model for poultry egg production. The motivation stems from analyzing the non-linearity and imprecision in egg production which is best handled by fuzzy logic as compared to other soft computing techniques. Data samples were collected on the age of chicks, quantity of feeds, quality of feeds, body weight of chicks and total egg production as input into the proposed system. The first stage of implementation is carried out in the Statistical Package for Social Science (SPSS) software environment to perform correlation analysis while the last two stages are carried out using Fuzzy Toolbox in MATLAB based on its capability to model and simulate complex systems with robust and interpretable predictions. Gaussian membership function was used in the fuzzification process while Mamdami structure was adopted as the fuzzy inference system (FIS). Model performance evaluation showed that the proposed system performed excellently by achieving best prediction accuracy 100% Pred(30) with an approximate mean magnitude relative error (MMRE) of 0.11744 as compared to the results in the reported literature. The method has proven to be a cost-effective and simple approach to modelling through the use of samples and parameters from poultry house.

181 Enhancing the efficiency of poultry production by optimising selection objectives and breeding strategies

Abstract Poultry, in general, have been the most successful farmed species in efficiently producing protein, be it egg laying chickens or meat producing chickens or turkeys. This has been the result of 60–80 years of continuous selection and improvement of traits that effect overall efficiency. The innate physiology of birds, high reproductive rates and relatively short generation intervals have helped speed these improvements. The contribution of feed to production costs has increased efforts to accurately measure feed intake, and selecting for efficient feed utilisation have been significant. Selection for feed efficiency forms an integral part of the breeding objective of the respective breeding programs, but in recent years other components of efficiency have become increasingly important within breeding objectives. Market segmentation, with greater volumes of fully deboned meat for further value added processing, has resulted in the optimisation of yield to feed for both broilers and turkeys. Allometric growth patterns, particularly late stage deposition of breast meat, gives rise to opportunities for optimisation of both slaughter age and weight. Genetic evaluations incorporating genomic information from birds with yield measurements is now implemented across both broilers and turkeys in many breeding programs. The implementation of genomic selection has benefits for not only yield, but also growth and feed efficiency, and we have shown improvements in genetic prediction of 11%, 23% and 31% for growth, breast meat and residual feed intake in turkeys, respectively. Reproductive efficiency has also been improved in broilers, turkeys and particularly layers, by improving evaluations using genomic information. Altering egg production curves to maintain production persistency for increasingly longer time periods has also been important. Layer flocks are now routinely in production for up to 500 days and similar objectives are used for selection in both broiler and turkey reproduction.

Genetic Evaluation of Egg Production Curve Parameters and Their Association with Some Economical Traits in Quail

Genetic Evaluation of Egg Production Curve Parameters and Their Association with Some Economical Traits in Quail

Genome-wide association analysis of egg production performance in chickens across the whole laying period

BackgroundEgg production is the most economically-important trait in layers as it directly influences benefits of the poultry industry. To better understand the genetic architecture of egg production, we measured traits including age at first egg (AFE), weekly egg number (EN) from onset of laying eggs to 80 weeks which was divided into five stage (EN1: from onset of laying eggs to 23 weeks, EN2: from 23 to 37 weeks, EN3: from 37 to 50 weeks, EN4: from 50 to 61 weeks, EN5: from 61 to 80 weeks) based on egg production curve and total egg number across the whole laying period (Total-EN). Then we performed genome-wide association studies (GWAS) in 1078 Rhode Island Red hens using a linear mixed model.ResultsEstimates of pedigree and SNP-based genetic parameter showed that AFE and EN1 exhibited high heritability (0.51 ± 0.09, 0.53 ± 0.08), while the h2 for EN in other stages varied from low (0.07 ± 0.04) to moderate (0.24 ± 0.07) magnitude. Subsequently, seven univariate GWAS for AFE and ENs were carried out independently, from which a total of 161 candidate SNPs located on GGA1, GGA2, GGA5, GGA6, GGA9 and GGA24 were identified. Thirteen SNP located on GGA6 were associated with AFE and an interesting gene PRLHR that may affect AFE through regulating oxytocin secretion in chickens. Sixteen genome-wide significant SNPs associated with EN3 were in a strong linkage disequilibrium (LD) region spanning from 117.87 Mb to 118.36 Mb on GGA1 and the most significant SNP (rs315777735) accounted for 3.57% of phenotypic variance. Genes POLA1, PDK3, PRDX4 and APOO identified by annotating sixteen genome-wide significant SNPs can be considered as candidates associated with EN3. Unfortunately, our study did not find any candidate gene for the total egg number.ConclusionsFindings in our study could provide promising genes and SNP markers to improve egg production performance based on marker-assisted breeding selection, while further functional validation is still needed in other populations.

Evaluation of egg production in Italian white geese in their first year of reproduction

Abstract The aim of this study was to analyse egg production traits in one-year-old Italian white geese based on individual monitoring of reproduction over the 6-week peak of egg laying. The study was conducted on 240 one-year-old female birds divided at the beginning of the reproductive season into 48 experimental subgroups, with 5 birds in each subgroup. During the 6-week production peak, the weight and number of eggs laid by each female were recorded on a daily basis. This allowed for the calculation of the rate of egg laying and plotting an egg production curve illustrating individually monitored performance of birds. The total reproductive season of geese lasted 150 days. During this period, the geese laid 13240 eggs, which were 55 eggs per average laying bird. The analysis revealed that during the 6-week monitoring of egg production at the peak of the season, the egg weight, number of eggs and rate of egg laying were highest in week 11 (150.4 ±Â 12.6 g; 3.5 ±Â 0.5 eggs and 50.0%, respectively). In week 11 of the production peak the laying performance of geese was highest (2.5–4.5 eggs) in most of the experimental subgroups.

Modelling cumulative egg production in laying hens and parent stocks of broiler chickens using classical growth functions

ABSTRACT1. The objective of the present study is to introduce fresh insight into modelling of egg production by applying classical growth functions to egg production records reported by Aviagen Management Guide to laying hens and the parent stock of broiler chickens.2. The functions (monomolecular, logistic, Gompertz, Richards and Morgan) were fitted using nonlinear regression procedures of SAS software, and their performance was assessed using goodness-of-fit statistics (coefficient of determination, residual mean squares, Akaike information criterion and Bayesian information criterion).3. Overall, except for the logistic and Gompertz, the growth functions evaluated gave an acceptable fit to the cumulative egg production curves, with the Morgan equation ranking first followed by the Richards equation. The Morgan and Richards equations provided satisfactory predictions of weekly egg yield at different egg production stages, from early to late production, whereas the least accurate estimates were obtained with the logistic equation.4. In conclusion, classical growth functions proved feasible alternatives to fit cumulative egg production curves of laying hens and parent stock of broiler chickens, resulting in suitable statistical performance and accurate estimates of production.

Phenotypic study of egg production curve in commercial broiler breeders using Compartmental function

ABSTRACT The objective of this study was to analyze the effect of environmental factors on egg production curve traits in broiler breeders. The weekly egg production records of birds collected from four different flocks were used to estimate the egg production curve parameters using the Compartmental function (y = A(1 − exp−b(t − d))exp−ct). The coefficient of determination (R2) obtained by the model was 0.98. Least squares analysis of variance indicated that the environmental factors such as hatchability and flock [...]

Egg production curve fitting using least square support vector machines and nonlinear regression analysis

Egg production curve fitting using least square support vector machines and nonlinear regression analysis