جستجوی مقالات مرتبط با کلیدواژه « مرغان تخم گذار » در نشریات گروه « علوم دام »

 It has been estimated that feed is the major cost associated with commercial poultry production. Conventional plant proteins, such as soybean meal tend to increase the poultry production costs due to limitation in cultivation in some region and therefore, their being transported from other countries. Hence, inclusion of non-conventional feed resource becomes of primary importance in poultry production to maintain the productivity at a lower cost. Black seed (Nigella sativa) referred to an important medical crop in many countries and is primarily consumed as medical oil. Black seed meal (BSM) is by-product after oil removal and can be used as a protein-rich meal (21). Black seed meal contains about 31.75 % crude protein and 19.37 % ether extract and it can be used as good sources of protein and energy (21) and substitution of soybean meal in laying hens practical diets. However, reports on the use of BSM in laying hens diets in second cycle of production are not enough, therefore, in this study, we considered the effect of different levels of black seed meal on laying hens performance in second production cycle.

Materials and Methods :

This experiment was conducted to determine the fatty acids composition of black seed meal and its effects on performance and egg qualitative traits of laying hens in second production cycle. In the first step, Gas-chromatography was used for determination the fatty acids composition of BSM. In the second step, a total of One hundred and twenty Hy-Line W-36 leghorn hens were housed in cages and randomly allocated to 5 treatment groups for 8 weeks. Each group was divided in to 4 replicates. Feed and water were provided ad libitum. The hens received basal diet (corn and soybean meal based diet with 14.80 % crude protein and 2813 kcal/ kg metabolizable energy) that was formulated to meet the Hy-line W-36 requirements recommended for nutrients. The diet did not have any antibiotics and coccidiostats. Dietary treatments were included 0, 5, 10, 15, and 20 percentage of BSM by expense of Soybean meal in basal diet and clarified as BSM0 (control), BSM5, BSM10, BSM15 and BSM20 respectively. Experiment was designed in a completely randomized design. Egg weight (gr), egg production (%) and egg mass (gr/hen/day) were recorded daily. Feed consumption was measured weekly and feed conversion ratio (FCR) (grams of feed: grams of egg mass) was calculated weekly too. At the end of 2, 4, 6 and 8 weeks of the experiment, two eggs from each replicate were randomly selected for measurement the egg qualitative traits. In this paper laying hens performance factors and egg quality parameters were calculated in three phase, the first 4 weeks, second 4 weeks and total period. Blood serum characteristics were evaluated in the end of experiment by bleeding from laying hens wings.

Results and Discussion:

 The results of this experiment showed that linoleic acid (41.51), oleic acid (34.67 %), palmetic acid (16.12 %), stearic acid (5.59), and linolenic acid (1.97) were the major BSM fatty acids. As it clear, the linoleic acid and oleic acid are most abundant fatty acid (unsaturated) in BSM. Feed intake was decreased by inclusion of BSM in laying hens diet and minimum feed intake was observed in BSM20. It seems to be the bitter taste of black seed meal is responsible for the decline of feed intake. It was reported that BSM has anti-nutritional substance and alkaloids (saponins), that these factors can reduce feed intake when used in high amount in poultry diets (21 and El-Dakhakhny, 1996). Laying hens egg mass was decreased in BSM10, BSM15 and BSM20 groups when compared with control group. Egg mass was calculated by multiplying egg weight in egg production. Reduced of feed intake can affect availability of nutrient for egg production and egg weight, and, therefore, egg mass will be decrease. Scientists reported that BSM can increase laying hens’ body weight and increasing body weight can decrease egg production. Laying hens’ serum cholesterol and triglyceride content were decreased significantly in BSM5 when compared with other groups. It was reported that essential oils in BSM can reduce activity of HMG-CoA (3-hydroxy-3-methylglutaryl coenzyme A) reductase that has vital role in cholesterol synthesis (Crowell, 1999).

Conclusion :

According to the results of this experiment, using BSM in 5 percent can reduce blood cholesterol and triglyceride without adverse effect on performance parameters.

New alternatives for feed supplementation in poultry production are considered to improve animal health and quantitative and qualitative productivity by the phytogenic feed additives. Essential oils are a mixture of several compounds of herbal origin that can be used for animal feeding to improve its performance and health. Essential oils can act as stimulant agents of the immune system during acute or chronic inflammatory processes that can be characterized by an increase on the levels of serum globulins, which can express the metabolic and nutritional status of the animal. Moreover, essential oils may improve nutrient digestion and absorption by enzymatic stimulation and they also may exert positive effects when used in laying hens. The use of natural capsaicin extracted from red pepper has preventive effects on salmonella infection in laying hens. Cinnamaldehyde and carvacrol have also been shown to stimulate mucin secretion, thereby preventing the establishment of pathogens. These compounds in marjoram, pepper, thyme are antimicrobial agent against Clostridium spores. The purpose of this study was to evaluate the combination of essential oils of carvacrol, cinnamaldehyde, capsaicin on performance, egg quality, microbial population, blood parameters, immunity and reproductive parameters of laying hens.

This experiment was done with 120 laying hens (Hy-line W-36) at 70 weeks of age for 10 weeks including 2 weeks of habituation and 8 weeks of sampling in a completely randomized design with 5 treatments, 4 replicates and 6 birds per replicate. Experimental treatments included control diet without additives and diets containing 50, 100, 150 and 200 mg/kg phytogenic supplement containing carvacrol 5%, capsaicin 2% and cinemaldehyde 3% (made by Pancosma, France). Egg weight (gr), egg production (%) and egg mass (gr/hen/day) were recorded daily. Feed consumption was measured weekly and feed conversion ratio (FCR) was calculated weekly too. At the end of weeks 2, 4, 6, and 8 of the experiment, two eggs from each replicate were randomly selected for measurement the egg qualitative traits. To investigate the effect of phytogenic supplementation on the immune system, at the beginning of 6 and 8 weeks of the experiment, 0.5 ml of 20% sheep red blood cell suspension (SRBC) in saline phosphate buffer (PBS) was injected into the breast muscle of one bird from each replicate. After 7 days of injection, blood samples was analyzed for SRBC titer. At the end of the study, one birds per replicate were killed by cervical dislocation and blood was drawn from the jugular vein. Serum was separated after centrifugation at 4500 g and 4°C for 10 min, and frozen at −20°C until further analysis was conducted for blood biochemical parameters. Serum samples were analyzed for concentrations of low-density lipoprotein (LDL) and high-density lipoprotein (HDL), cholesterol, glucose and triglycerides using standard kits (Zist Shimi, Tehran, Iran) with an autoanalyser (Autolab PM 4000; Medical System, Rome, Italy). Then, caecal digesta (1 g) from each bird were aseptically transferred into 9 ml of sterile saline solution and serially diluted. Lactobacilli, Coliforms, and E.Coli were grown on Rogosa–Sharpe agar, MacConkey Agar, and Eosin Methylene Blue Agar, respectively. Plates for Lactobacillus were incubated anaerobically for 48 h at 37 °C. Microbial populations for E. coli and Coliforms were counted after aerobic incubation at 37°C for 24 hours. All samples were plated in duplicate.

The results of these experiments showed Feed intake and feed conversion ratio of the laying hens were not affected by dietary inclusion of different levels of phytogenic supplement. During the whole period of experiment, 100 mg/kg phytogenic supplement significantly increased egg weight and egg production. Egg quality traits including egg unit, yolk color, shell strength, shell weight, yolk weight, were not affected by phytogenic supplement but eggshell thickness of birds fed on 100 mg/kg of phytogenic feed additive increased significantly (P < 0.05). Cecal population of Lactobacillus was increased (P < 0.05) in birds fed various levels of phytogenic feed additive compared to control (P < 0.05). Blood parameters of glucose, cholesterol, triglycerides, HDL and LDL were not affected by the treatments. Different levels of phytogenic supplementation did not show significant effect on primary and secondary antibody response and reproductive parameters of laying hens. The results showed that not only the addition of 100 ppm of phytogenic additive to the diet of laying hens improves egg production percentage, egg weight and egg shell thickness, but also, increased cecal population of Lactobacillus.

With using multiple linear regression (MLR), can simultaneously analyses several different variables, but to get the desirable results from the MLR, the samples must be much and accurate. Therefore, this method has high sensitivity and may cause errors in results. In addition, to use this method, the variable must have normal distribution and modification follow from a linear relationship. Artificial Neural Network (ANN) technique is used to solve a wide range of problems in science and engineering, particularly for some areas where the mathematical modeling methods fail. Nowadays, the ANNs are one of the most powerful modeling techniques to model complex nonlinear, multidimensional function relationships without any prior assumptions about the nature of the relationships. Artificial neural network models are different from mathematical modeling approaches in their ability to learn relationships between dependent and independent variables through the data itself rather than assuming the functional form of the relationships. A well trained ANN can be used as a predictive model for a specific application. The prediction by a well-trained ANN is normally faster than the mathematical models. Several authors have shown greater performances of ANN as compared to regression models. An ANN model can predict multiple dependent variables based on multiple independent variables, where a mathematical model is only able to predict one dependent variable at a time. Therefore, this study was designed to evaluate the prediction of production performance of laying hens using the neural networks and nonlinear regressions.

Review the four consecutive, information were obtained from a laying hen farm. Data mining methods include: three-layer perceptron neural network, four-layer perceptron neural network, radial basis function (RBF) neural network and multiply linear and nonlinear regression. In linear model, the variables of age flock, month of production, feed intake have been considered as the predictor variable and production (percent and egg mass production and feed conversion ratio) have been considered as the response variable. Three steps were taken to select an optimal ANN model. The first step was to determine the best number of hidden layers, number of neurons in each hidden layer, and activation function. The best models were selected on the basis of training and prediction accuracy. The second step was to work with the selected models to find the optimum epoch size. The third step was to find the optimum learning rate and momentum values. The evaluating method for selecting the optimal ANN was based on the minimization of deviations between predicted and measured values.

The aim of this study is to obtain an ANN model with minimum errors in training and testing. Nonlinear regression models were compared with Neural Network models. All the models are compared using the coefficient of determination (R2) and Mean Absolute Error (MAE). The results showed that the artificial neural networks compared the regression models and between different artificial neural networks the RBF model had better curve fitting for laying hen production performance indicators included; egg production (% / b/ d), egg mass (g/ b/ d) and feed conversion ratio in front of age and this fact shows that even for spiral data artificial neural network works well. Therefore, we can use these models for complex situations.

The obtained results revealed that the ANN model may efficiently be fitted into the laying hen production performance include percentage and egg mass and feed conversion ratio of hen flocks. Results showed that the method of radial basis function (RBF) neural networks acts better than other models in predicting the production performance of laying hens. So we can conclude RBF model performed better predict laying hen performance.