linear regression

In many countries of the world, the camel has played an important role in the lives of people in arid and semi-arid regions in terms of providing milk and meat products. Based on phenotypic characteristics, the camel has been adapted to the deserts from physiological, anatomical, and behavioral points of view. Due to limited water resources, the south Khorasan province of Iran has not been an appropriate geographical region for a great number of agricultural products. Weighing plays an important role in the management of camel breeding to adjust the nutritional needs and also the annual evaluation of the animals. One of the main challenges of camel breeding is the difficulty of recording and the lack of records due to the wild nature and also the large size, especially in adults. Due to the many difficulties and risks, camel breeders usually use various potential alternative tools such as apparent estimation or weighing tape to estimate the weight of camels at different ages. For farm animals, significant correlations between body measurements and body weight can be used as a tool to estimate the weight of animals through a mathematical equation. The main objective of this study was to compare the accuracy of artificial neural network (ANN) and multiple linear regression model (MLR) in estimating the body weight of a humped camel.
In the present study, the data of a total number of 177 one-humped camels in four groups  (including 1. 63 adult female Pakistani camels aged between 9 and 12 years, 2. 21 adult female Baluchi camels aged between 9 and 12 years, 3. 93 male and female camels less than 2 years of age of Pakistani and Baluchi breeds, and 4. total camels) collected at the South Khorasan camel breeding station in 2019 were used. Morphological characteristics of camels include: neck length, neck girth, hand length, foot length, shoulder height to the ground, hump height to the ground, hip height to the ground, chest girth, chest width, abdomen girth, hip width, tail length, breast height, and breast girth were measured. After measuring the body dimensions, the evaluated camels were weighed using a 1000 kg digital scale. To estimate the weight of camels from their body dimensions, the data were analyzed using the MLR model and ANN with the nnet package in R software. For ANN analysis, 80% of the data were considered for network training and 20% for testing. The accuracy of MLR and ANN for camel body weight estimation was compared using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).
The results of the MLR model showed that body dimensions of shoulder height to the ground, chest girth, abdominal girth, neck girth, and hand length had a significant effect on body weight (P<0.05). In the ANN model, chest girth, abdominal girth, and shoulder height to the ground were the most important variables in estimating the body weight of camels of the whole population. The MLR and ANN models had acceptable accuracy in weight estimation. However, compared to the MLR model, the ANN model had a higher R2 and a lower error in estimating the weight of camels. In the first group, R2 and RMSE were found to be 0.996 and 6.852, respectively, for ANN while the corresponding values were 0.979 and 22.955, respectively, for MLR.  In the second group, R2 and RMSE were found to be 0.995 and 3.525, respectively, for ANN while the corresponding values were 0.989 and 5.377, respectively, for MLR.  In the third group, R2 and RMSE were found to be 0.896 and 13.959, respectively, for ANN while the corresponding values were 0.849 and 17.549, respectively, for MLR. In the fourth group, R2 and RMSE were found to be 0.929 and 20.248, respectively, for ANN while the corresponding values were 0.903 and 38.505, respectively, for MLR. The results showed that ANN is more accurate compared to MLR in predicting the body weight of camels. 
In terms of goodness of fit (including R2 and RMSE), the results of the present research suggest that both MLR and ANN methods have high acceptable accuracy for predicting body weight in camels. ANN was more suitable compared to MLR, suggesting that it could be used to predict camel body weight. Furthermore, grouping the camels by age and breed could also lead to higher precision and lower prediction error.

In order to determine of optimum Holstein inheritance on traits performance of dairy cattle, 305-days milk production records were used 20000 Holstein cows from Markazi province. Data were collected during 1999 to 2013 by Animal Breeding Center of Iran. Generalized linear model (GLM) procedure of SAS 9.1 software was used to analyze of fixed effects (herd, month of calving, year of calving and sperm type, inheritance) and calving age as covariate on production traits. Study on Holstein cow with different ratio of inheritance was done that includes: 12, 25, 50, 62, 68, 75, and 84,87,90,92 percent. Phenotypic and genetic (co) variance components for production traits with Wombat software were estimated. Then, a selection index was constructed for combine these traits. Optimum Holstein inheritance based on this index was determined by least squares mean comparisons of the index for different Holstein inheritance and simple linear regression. Results indicated 84-92 percent Holstein inheritance was the optimum level. Optimum inheritance on milk production, amount fat and protein has positive effected and it is increased the average production.

In order to measure some of the blood serum biochemical parameters in normal rearing conditions and determination of their relationship with body weight and carcass components and also the examination of sex effect on blood biochemical parameters in Isfahan native chicken, an experiment was conducted using 200 one-day-old native chickens. The chicks were reared in completely identical conditions for 12 weeks. At the end of week 12 and after six hours of fasting, the chicks were slaughtered, and their body weight and various carcass components were measured. The cholesterol, triglyceride, total protein, albumin, uric acid, calcium, phosphorus, malondialdehyde, high density lipoprotein and glucose concentrations were measured using commercial kits. The amount of globulin was calculated as the difference of total protein and albumin. The results showed that the highest significant positive correlation (P<0.05) was observed between cholesterol with body weight and thigh weight (0.21) and the highest significant negative correlation was observed between uric acid concentration and liver weight, and between calcium and breast weight (-0.24). The multiple regression analysis showed that cholesterol, globulin and phosphorus parameters could be used to predict body and breast weights, as well as cholesterol and globulin levels for predicting of carcass and thigh weights. In addition, there was no significant difference between two sexes in any of the measured biochemical parameters. According to the results of this experiment, blood parameters could be used to predict some carcass characteristics in Isfahan native chicken.

This experiment was conducted to determine in the threonine requirements laying japanese quail. A total of 240 female Japanese quails were used from 10 to 17 weeks of age, in a completely randomized design (CRD). Experimental treatments consisted of three treatments: basal diet (0.69% threonine), basal diet+0.05% L- threonine (0.74% threonine) and basal diet+0.1% L- threonine (0.79% threonine), with 4 replicates and 20 quails in each. Feed intake, egg production, egg mass, feed conversion ratio and body growth rate were recorded during this study. Egg quality traits were measured biweekly. Performance parameters were not affected by supplementation L- threonine. Quail requirement for threonize was evaluated using, linear regression and quadratic equation. The lowest feed intake and the highest thin albumen of height showed by 0.74% dietary threonine with a quadratic trend. Albumen height, albumen index, yolk color and Haugh unit were decreased linearly by increasing threonine levels. Based on these results, it is concluded that the 0.69% threonine level in the diet was enough to achieve the potential of egg performance in of laying Japanese Quail.

To determine the amount of food amino acid and to spend time in the laboratories are expensive & time-consuming due to a chemical analysis. In the current laboratories, digestion NIRS method is widely used for this purpose. But this method has technical limitation. Therefor is important find appropriate method for estimate amount of amino acids. Artificial Neural Network (ANN) can provide a better reflection of the relationship between approximation feed composition and particular nutrient amount in that feed. Therefore, this study was performed to estimate amino acids corn and wheat by using artificial neural networks and multiple linear regression (MLR). In neural models used in the study, input variables include crude protein, crude fat, crude fibre, phosphorus and ash, and output variables includ profiles of amino acids relevant to combination of these two types of feed. The Results showed that there is a significant relationship Between amino acids in corn and wheat and its chemical composition. Also The statistical evaluation showed that the ANN model compared with MLR was a stronger estimation for prediction the amount of each amino acids. Hence the artificial neural network as a powerful tool for modelling, forecasting and estimating the nutrient composition of foods used poultry. Using the results of this study, it is recommended that artificial neural network can be used as a computational method with sufficient accuracy for modelling, prediction and estimation of the nutrient composition of foods used in poultry.