Performance and welfare of different genetic groups of laying hen

Valentim, Jean Kaíque; Lima, Heder José D’Ávila; Bittencourt, Tatiana Marques; Barros, Fernanda Keller Queiroz; Braga, Jefferson Darlan da Costa; Antunes, Hellen Cristina Ferreira

doi:10.4025/actascianimsci.v41i1.42904

Cited by 5 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Valentim et al (2019), genetics and breeding researches are necessary to know phenotypes, lines, breeds, and patterns, to find which categories are more efficient for each production system, since these variables are intrinsically related to their zootechnical performance characteristics.…”

Section: Resultsmentioning

confidence: 99%

Physical quality of eggs of four strains of poultry

Almeida

Mendonça

Weitzel

et al. 2021

Acta Sci. Anim. Sci.

Self Cite

View full text Add to dashboard Cite

The layer poultry is an important activity for food production with high biological value. Measuring egg quality has great relevance to ensure safety and quality products for consumers. Thus, the objective was to evaluate the egg physical quality of four laying hen’s strains. Were used 864 eggs from four laying hens’ lines (Hisex Brown®, Hy-Line Brown®, Isa Label®, and Lohmann Brown®). The experimental design was completely randomized composed of four treatments (strains) with nine replications with four eggs each. Egg weight, egg diameter, egg length, specific gravity, yolk, albumen and shell weight and percentage, Haugh units, and shell thickness were evaluated. There was a significant difference for all parameters evaluated. The Hisex Brown® strain showed the best results for egg diameter, egg length, specific weight, albumen height, Haugh units, yolk weight, albumen%, shell weight, shell%, and shell thickness, while the Hy-Line Brown® produced bigger and heavier eggs, and Isa Label® presented the highest yolk%. The Hisex Brown® strain showed the better physical quality of eggs when compared to the other studied lines, the strain being indicated when the objective is to produce eggs with better internal and external quality.

show abstract

Section: Resultsmentioning

confidence: 99%

Physical quality of eggs of four strains of poultry

Almeida

Mendonça

Weitzel

et al. 2021

Acta Sci. Anim. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Despite presenting the lowest R 2 , this network has a good prediction capacity. The available data used for the training and validation of RNAs do not distinguish white and red lineages, which clearly have different feed consumption [25]. So, this data field characteristic must be the cause of a less accurate model.…”

Section: Discussionmentioning

confidence: 99%

“…For this variable, it is important to highlight that white and red lineages were not distinguished in the database. Although different genetic groups of layer hens present variable growth rate under the same environmental and nutritional conditions [25], this aspect does not impact the accuracy of the ANN.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks on Eggs Production Data Management

Almeida

Oliveira

Furian

et al. 2020

Acta Scientiae. Vet.

View full text Add to dashboard Cite

Background: Eggs have acquired a greater importance as an inexpensive and high-quality protein. The Brazilian egg industry has been characterized by a constant production expansion in the last decade, increasing the number of housed animals and facilitating the spread of many diseases. In order to reduce the sanitary and financial risks, decisions regarding the production and the health status of the flock must be made based on objective criteria. The use of Artificial Neural Networks (ANN) is a valuable tool to reduce the subjectivity of the analysis. In this context, the aim of this study was at validating the ANNs as viable tool to be employed in the prediction and management of commercial egg production flocks.Materials, Methods & Results: Data from 42 flocks of commercial layer hens from a poultry company were selected. The data refer to the period between 2010 and 2018 and it represents a total of 600,000 layers. Six parameters were selected as “output” data (number of dead birds per week, feed consumption, number of eggs, weekly weight, weekly egg production and flock uniformity) and a total of 13 parameters were selected as “input” data (flock age, flock identification, total hens in the flock, weekly weight, flock uniformity, lineage, weekly mortality, absolute number of dead birds, eggs/hen, weekly egg production, feed consumption, flock location, creation phase). ANNs were elaborated by software programs NeuroShell Predictor and NeuroShell Classifier. The programs identified input variables for the assembly of the networks seeking the prediction of the variables called outgoing that are subsequently validated. This validation goes through the comparison between the predictions and the real data present in the database that was the basis for the work. Validation of each ANN is expressed by the specific statistical parameters multiple determination (R2) and Mean Squared Error (MSE). For instance, R2 above 0.70 expresses a good validation. ANN developed for the output variable “number of dead birds per week” presented R2= 0.9533 and MSE= 256.88. For “feed consumption”, the results were R2= 0.7382 and MSE= 274.56. For “number of eggs (eggs/hen)”, the results were R2= 0.9901 and MSE= 172.26. For “weekly weight”, R2= 0.9712 and MSE= 11154.41. For “weekly egg production”, R2= 0.8015 and MSE= 72.60. For “flock uniformity”, R2= -2.9955 and MSE= 431.82.Discussion: From the six ANN designed in this study, in five it was possible to validate the predictions by comparing predictions with the real data. In one output parameter (“flock uniformity”), it was not possible to have adequate validation due to insufficient data in our database. For “number of dead birds per week”, “feed consumption”, “weekly weight” and “uniformity”, the most important variable was “flock age” (27.5%, 52.5%, 55.2% and 37.9%, respectively). For “number of eggs (eggs/hen)”, “uniformity” (52.1%) was the most relevant variable for prediction. For “weekly egg production”, “flock age” and “number of eggs (eggs/hen)” were the most important zootechnical parameters, both with a relative contribution of 38.2%. The results showed that even with the use of a robust tool such as ANNs, it is necessary to have well-noted and clear information that expresses the reality of the flocks. In any case, the results presented allow us to state that ANNs are capable for the management of data generated in a commercial egg production facility. The process of evaluation of these data would be improved if ANNs were routinely used by the professionals linked to this activity.

show abstract

“…Besides nutrition, environmental factors also interfere in the quality of the eggshell. This includes environmental temperature, since high temperatures interfere in the metabolism of eggshell formation by increasing respiratory rate, which induces respiratory alkalosis and can reduce feed intake (Valentim et al, 2019). The age of birds is also a determining physiological factor in eggshell quality, since aging increases egg weight, but the amount of calcium carbonate deposition remains practically constant during the productive life of laying hens, resulting in a reduction in eggshell thickness.…”

Section: Introductionmentioning

confidence: 99%

Organic minerals, tributyrin, and blend of organic acids in the diet of commercial laying hens at the end of production

Miranda¹,

Moreira²,

Almeida³

et al. 2023

SA J. An. Sci.

View full text Add to dashboard Cite

This study aimed to evaluate the responses in performance, egg quality, and health of internal organs of laying hens of Hisex White lineage. The birds were provided with diets supplemented with organic minerals (OM) + tributyrin, associated or not with a mixture of organic acids (benzoic formic, citric, and phosphoric), compared to a control treatment containing zinc bacitracin (28 ppm). In total, 160 laying hens of the commercial Hisex White lineage of 68 w and an average weight of 1.735 ± 0.025 kg were distributed in 40 experimental plots. The study employed a completely randomized design, with five treatments and eight replications/treatment. The experimental period was divided into seven production cycles of 21 days, totalling 147 experimental days. The variables analysed were egg production and loss, feed intake, mean egg weight, egg mass, feed conversion, and internal and external quality variables of eggs. At the end of the experimental period, the final weight of the birds was measured to evaluate the relative weight of liver and kidneys. No significant effect of the treatments on the productive performance and internal and external quality of the eggs was evident. The association of organic minerals with a mixture of organic acids and tributyrin did not influence the productive performance and internal and external quality of the shell of Hisex White laying hens in the period from 68 to 89 weeks of age.

show abstract

Performance and welfare of different genetic groups of laying hen

Cited by 5 publications

References 14 publications

Physical quality of eggs of four strains of poultry

Physical quality of eggs of four strains of poultry

Artificial Neural Networks on Eggs Production Data Management

Organic minerals, tributyrin, and blend of organic acids in the diet of commercial laying hens at the end of production

Contact Info

Product

Resources

About