مدل سازی

This study aimed to investigate the effects of different levels of synbiotic on performance, intestinal microbial population and morphology of broiler chicks subjected to chronic heat stress and estimation of optimal point of synbiotic using broken line modeling. In total, 240 one-day-old (Ross 308) broiler chicks were allocated into four experimental treatments with five replications (12 chicks) in complete random design. Treatments containing 0, 0.5, 1.0 and 1.5 (g/Kg) synbiotic added to experimental diet from 3 to 42 days. Chronic heat stress was induced from 28 to 42 days of age to all groups (32 degree Celsius for 6 hours, from 10:00 am to 4:00 pm).The results showed that synbiotic improved body weight gain and feed intake significantly to 28 day and during chronic heat stress (p≥0.05). So that, the groups receiving 1 and 1.5 (g/kg) of synbiotic had the highest performance. Treatments affected microbial population and caused increasing lactic-acid producing bacteria (p≥0.05). Moreover, the result showed that treatments made significant difference in intestinal morphology and increased villus height (p≥0.05). European production index significantly increased the groups receiving 1 and 1.5 (g/kg) of synbiotic (p≥0.05). The results of this study showed that using synbiotic during chronic heat stress, improved performance through positive changes in morphology and microbial population of intestine. Based on linear and quadratic broken line modeling, the optimal point of synbiotic are 1.3 and 1.42 (g/Kg diet) respectively.

In order to compare the performance of neural network with some mathematical functions for predicting of lactation curve of Iranian Holstein dairy cows, a total of 1,085,525 milk test day records from first-parity dairy cows calved during 1983-2012 were used. Fitting the lactation curve was performed by brnn package (for neural network) and also by some mathematical functions (including Wood, Wilmink, Ali-Schaeffer and Pollott-Gootwine) using R software based upon average milk yield, fat and protein percentage test day records. The criteria of AIC, BIC, RMSE and adjusted R2 were utilized to evaluate goodness of fit. The results showed that the Bayesian neural network (brnn) had a better fit than mathematical functions in describing the standard curve shape of Iranian Holstein dairy cows. Among the mathematical functions used for milk yield, Wilmink model had a better fit while for milk fat percentage and milk protein percentage, Ali-Schaeffer model showed a better fit performance. Therefore, it could be suggested that brnn is an appropriate option to be applied to fit the lactation curve of Iranian Holstein dairy cows.

A study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical techniqueAbstract While the physiological conditions of most animals are reflected in their body fluids, in dairy cows physiological conditions are reflected in milk combinations. When negative energy balance occur, production that caused body fat store destruction, goes in two major metabolic ways in liver and breast tissues. In breast tissues, free fatty acids increase fat to protein ratio and in liver, free fatty acid change metabolism leading to increase of ketone bodies. Lactation persistency in dairy cows is one of inheritable economic trait and important characteristic of the lactation curve. The change of this trait is under control of different factors such as animal genetics structure. The amount of cow production at the peak of milk production depends on the calving physical condition, genetics, the absence of infectious and metabolic diseases, and postpartum diet. Fat to protein ratio in early lactation is associated with performance during lactation. Fat to protein ratio is also correlated with some diseases such as retained placenta, displacement of abomasum, metritis, endometritis, mastitis and culling. Fat to protein ratio (FPR) in test day milk records is a good indicator for body fat metabolism and the level of negative energy balance, and that it could be used as a selection criterion to improve metabolic stability. Quantile regression has not been widely used for modeling of biologic characteristics of livestock and this research aimed to evaluate the shape of fat percentage, protein percentage and FPR in test day milk records of Iranian dairy cows using a polynomial quantile regression model. The data used in this study were provided by the Animal Breeding Centre, Iran. Initial data set were edited by Excel and Foxpro software. The traits under consideration were fat percentage, protein percentage and fat to protein ratio (calculated based on the magnitude of the first two traits). All cows had value for all traits. Final data comprised a total of 784,532 test day records collected from 93,259 first-parity dairy cows (progeny of 2741 sires and 79843 dams) distributed in 660 herds over the country and calved between 2003 and 2013. In the data, test day fat percentage and protein percentage had a minimum of 1 and 1.5 and a maximum of 7 and 7, respectively. By SPSS software, some statistical characteristics of the traits were calculated, and by SAS software a quadratic polynomial quantile regression model (in the range of quantiles 5 to 95) was fitted to the data. Based upon the fitted model, linear and quadratic regression coefficients were estimated for the effect of day of lactation on each trait in different quantiles. In the model, traits and day of lactation were response and independent variables, respectively. Quadratic effect of day of lactation is considered due to nonlinear variation of the traits over the course of the lactation curve. Mean fat percentage, protein percentage and fat to protein ratio over the lactation course were found to be 3.35%, 3.08% and 1.1, respectively. As expected, for each trait a nonlinear change was observed over the lactation period. The highest content fat to protein ratio occurs in the early period of lactation and the lowest was observed in the sixth and seventh months of lactation. In terms of absolute values, minimum (953.564) and maximum (1797.661) linear regression coefficient of days in milk were obtained in quantiles 5 and 95, respectively. The same trend was also observed for quadratic regression coefficient of days in milk. Linear and quadratic regression coefficients of fat and protein percentages ranged over different quantiles so that minimum and maximum absolute values were observed in quantiles 5 and 95, respectively. Quantile regression model is an appropriated statistical technique for evaluate the effects of an independent factor which differently affects the shape of a response variable. In this research, the effect of days in milk on three traits (fat and protein percentage, as well as fat to protein ratio) was modeled by quantile regression. The findings of the present research indicate that the curve shape of fat percentage, protein percentage and fat to protein ratio are different for the quantiles suggesting that these differences are needed to be taken into account in nutrition management and cattle breeding scheme.Keywords: Dairy cow, Modeling, Quantile regression, Ratio of milk compositionA study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical techniqueA study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical techniqueA study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical techniqueA study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical techniqueA study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical techniqueA study on the curve of far percentage, protein percentage and fat to protein ratio in test day milk records of Iranian dairy cows using quantile regression statistical technique

This study was conducted for predicting performance traits of the whole production period of Arian broiler breeder via its initial performances using artificial neural network (ANN). The input variables of modeling were house, number of hens in the house, weeks of production, body weight at 20-24 weeks of age and outputs of the model were body weight, egg number, egg mass, egg weight and feed intake at the 25 to 47 weeks of age. The used ANN model for prediction of body weight had 5 inputs, 5 neurons at 1st hidden layer, 2 neurons at 2nd hidden layer and 1 output, thus we write it as 5-5-3-1. Similarly, the optimized ANN model structure for feed intake, egg number, egg weight and egg mass were 7-7-4-1, 8-8-4-1, 7-7-3-1 and 7-7-3-1, respectively. R2 of adequate models for BW, FI, EN, EW and EM were 0.991, 0.998, 0.989, 0.993 and 0.996, and Root Mean Square Error were 1.55, 0.992, 0.266, 3.838 and 0.506, respectively. The results of the study shown that architecture and the specification of the neural networks such as inputs, outputs, number of neurons and number of hidden layers can affect the performance of the ANN model. The results indicated the possibility of predicting whole production period of Arian broiler breeder using early stage production records.

All breeds of Irannians sheep except Zel has a fat tail, and despite their lower carcass fat percentage, male lambs have higher fat-tail weight. Using within breed genetic variation requires accurate and precise measuring of fat tail weight on candidates of selection. The aime of this study was comparision of artificial neural network (ANN) modeling and linear modeling methods to prediction of fat tail weight, using body weight and different tail dimensions. 32 lambs of Chal and Zandi breeds,crosses of Zandi×Chal,Zel×Zandi and Zel×Chal hybrids were used for modeling to an estimation of fat-tail weight. Inputs of the model was birth type, sex, breed, upper width , mid width and lower width of fat tail,fat tail height and body weight, output of the model was fat tail weight. body weight, genotype, and fat tail mid-width had the largest positive correlations with fat-tail weight,0.83,-0.82 and0.80,respectively. The adequacy parameters of the best artificial neural network model had a coefficient determination of 0.99 and a mean squared error(RMSE)of 70.3g. The values of these estimated parameters by the multiple linear model were 0.891 and 263.86, respectively. The results of the extension of the original study showed the complexity of the interactions between the model inputs. Present research approved to accurate and unbiased estimation of tail weight of different breeds and crosses using artificial neural network. Furthermore, present study showed that ANN model can be used for accurate and presise estimation of fat tail weight using measured traits on sheep,than linear model.

Climatic variables are among the factors influencing the performance of various crop and livestock products. Considering the advanced climate change, the impact of climate indicators on livestock production can present serious challenge for producers. Crop yields prediction is one of the most important issues in the food security debate to increase the economic efficiency of livestock production. In this research, the relationship between climatic elements and indicators with honey bee function in Shahindej was investigated. The length of the statistical period was 12 years from 2007 to 2018. First, elements and climatic indices that were highly correlated with product yield were identified. Then, by default, the honey yield prediction model was presented. The validity of the model is based on the linearity of the relationship between independent and dependent variables, then the normal distribution of the error values and the independence of the error values. In the end, the normal distribution of the dependent variable was tested. Output of the model results showed that product performance with temperature was significant. The accuracy of the honey performance model with a RMSE was 11%. Therefore, it can be said with great accuracy that the effect of climate change on a half-degree increase in temperature will reduce honey performance by about 40 tons per year. The results of the study of the trend of cold and cold climate indicators presented by the World Meteorological Organization's Climate Change Detection Panel, Indicates that the region under study is experiencing a warming process. Therefore, changes in bee function were mainly influenced by climatic factors.

In this research, the artificial neural networks (ANNs) and multi-layered non-fuzzy inductive inference system (ANFIS) were used to model the output energy in dairy and beef cattle farms. For this purpose, according to Cochran's relation, 105 beef and dairy farms were randomly selected from five townships which were the main producers of this sector in Mazandaran province from 2016-2017. Using the energy balance of inputs and outputs, the input and output energy averages in beef production farms were calculated to be 16994.76 and 3449.85 MJcow-1 and for dairy production farms were equal to 100100 and 58277 MJcow-1, respectively. Also, ER (Energy Ratio), EP (Energy Productivity), SE (Special Energy) and NE (Net Energy) indices for dairy production farms were 0.58, 0.08 KgMJ-1, 12.5 MJKg-1 and -41825.93 MJcow-1, respectively and for beef production farms were calculated as 0.2, 0.02 KgMJ-1, 50 MJKg-1and 13544.91 MJcow-1, respectively. Using the results of statistical analysis of the data, modeling of the output energy for each unit of input energy was performed by two above methods (ANNs and ANFIS). The results showed that the model of nervous- fuzzy inference comparative multi-layered system with the correlation coefficient of 0.9899 for steer farms and 0.9933 for dairy farms, had better performance and accuracy than that of the artificial neural network with the correlation coefficient of 0.8118 and the structure of 6-16-1 for beef farms and correlation coefficient of 0.9837 and the structure of 5-12-1 for dairy farms.

Production systems in bio-Industries rest on resources management and conversion of various forms of energy. This research is investigated and modelled the energy of broiler production in a non-parametric form. The studied samples consist of 70 broiler productions in Ardabil province which were randomly selected from statistical society of the region. In this study, the equivalent energy consumption was estimated in the mentioned industry and the energy indices were calculated, then the equivalent amount of output energy and performance of system were modelled and estimated, using the artificial neural network models. Based on the obtained results, the total equivalent energy of input and output in broiler production were calculated as 153.79 and 27.45 GJ per 1000 birds, respectively. The most consumable input energy in the broiler production of region belongs to the fossil fuel with 61.48% of the total equivalent energy. Based on the results of artificial neural network, the best structure for modelling of energy consumption of broiler production was estimated 5-14-2 structure with five inputs, one hidden layer with 14 neurons and one output layer with two output parameters. The determination coefficient of the best weight combination for estimating the equivalent energy of broiler and manure production for testing data were obtained 93% and 91%, respectively and for validation data were obtained 98% and 95%. In assessing the effectiveness of inputs on the outputs, the fossil fuel showed the highest sensitivity among the production inputs that reveals the needs for revision of the energy resources more than ever.

In recent years, considerable progress in knowledge at different fields of life sciences has been occurred and resulted in discovery of important biological pathways associated with human diseases as well as important economic traits in animals. In a reaction mechanism involving many cells that are linked together. Product in each reaction can be used in the next reaction as a reactant and thus metabolic network can be formed. There are same fact about relationships between genes in regulatory networks and the products. Analysis and simulation of metabolic networks is one of the topics discussed in system biology that causes understanding molecular complex relationships in cell. The concept of systems biology is to identify mechanism of cell components or complete being and understanding growth and development paths. The discovery of this mechanism is possible using numerical data and analyzing the interaction of elements involved in the system that makes predicting the characteristics and performance of the components in biological pathways. The basic topics of this knowledge, the study of biochemical pathways and the use of different genomic information in identifying unknown paths and complete it. General purpose systems biology is modeling of biological processes and understanding the processes within cells in respond to changes in the environment. Use this knowledge to the test and characterization of isolated parts of a cell, the cell structure and dynamics of the total available will be checked.

The nutrient requirements and performance guidelines of management guides are based upon many actual flock results obtained under good environmental and management conditions. In practice and under the prevalent poultry industry situation، through these nutrient levels، Iranian farmers cannot attain the performance reported in management guidelines. An estimation of the model of broiler breeder energy requirement under environmental and management conditions of Iran was the aim followed in the present study. The study was carried out، making use of observational as well as analytical information. Data related to broilers of 24 to 64 weeks of age were collected from 24 broiler breeder farms which reared Ross 308. These farms were distributed throughout 11 provinces of Iran. The equation for estimating Metabolizable Energy Intake (MEI) was predicted (R2=0. 7) through: MEI (kcal/bird/day) = 153. 032 BW0. 75 + 6. 708 ADG + 14. 198 EM -2. 946 (BW0. 75× ADG) -4. 872 (BW0. 75× EM) -0. 272 (ADG × EM) + 0. 11 (BW0. 75× ADG × EM

The aim followed in the present study was to investigate the economic aspects of, and to derive Economic Weights (EWs) for Mature Body Weight (MBW) as well as for age at First Calving (AFC) in Holstein dairy cattle of Iran. Economic values (EVs) for traits were estimated using trait-by-trait bio-economic models. Absolute EVs were RLs -16400 and -17900 per cow per year for MBW and AFC, respectively. Because of the negative effects of these traits on profitability, EWs of -7540 for MBW and -3760 for AFC are suggested for a national selection index. A one day increase in AFC increased production costs by Rls 39600 per cow per year. Sensitivity analysis revealed that the EV for MBW was more sensitive to changes in live weight price than to feed costs. Results obtained in this study can be used in the development of breeding objectives and in the cost- benefit analysis of management programs which aim at changing the attitudes of farmers and of their consultants towards decreasing costs and therefore increasing profitability.