جستجوی مقالات مرتبط با کلیدواژه "sistani cattle" در نشریات گروه "علوم دام"

Sistani cows are generally restless animals; therefore, controlling, treating, and weighing them is difficult. On the other hand, recording the weight of domestic animals, including Sistani cows, is inevitable, because it provides a good scale for management decisions in the herd such as balancing the diet, changing environmental conditions, or determining the time of slaughter of fattening animals. In addition to scales, various methods are commonly used to measure the body weight of large animals. Some of these methods include the use of weight-meters, appraisal assessments, and the use of mathematical models. One of the new methods for predicting livestock weight is artificial intelligence. Because some reports are indicating that artificial intelligence could facilitate the weighing process of animals, this study was performed to predict the body weight of Sistani cows using computer vision technology. The data required for this study were recorded in the Zahak breeding station located in Sistan and Baluchestan province of Iran. The recording operation involved the weighing and biometric measurement of about 190 Sistani cattle, including calves, heifers, and male and female animals, every three months during a year. At the time of weighing, images of the lateral view of each animal were taken and recorded using the CANON SX150IS digital camera. During this period, a total of 358 weight records of Sistani cows at different ages were recorded. The digital images were initially preprocessed using MATLAB software, and then some morphological features were extracted from each image. For predicting the weight of Sistani cows via the Artificial Neural Network (ANN), the extracted features of images were introduced to the ANN model as input and the weight of cows as output. The "feed-forward neural network", which was trained by the "error propagation" algorithm, was used to predict the weight of cows. The function used in the hidden layer of the ANN model was sigmoidal and in the output layer was linear. An ANN model which had the highest precision and lowest error was selected as the final model for predicting the animal weights. The criteria for selecting the best model were the highest determination coefficient (R2) and the lowest mean square error (MSE) compared to other available models. Out of 22 features extracted from each image, only 15 of them, which had a higher correlation with the body weight of cows at different ages, were selected as effective features. As result, equivalent diameter, major axis length, minor axis length, bounding box, convex area, filled area, area, perimeter, and the number of non-zero pixels of the image (NNZ) had the highest correlation with the cattle weights (P<0.01) and used as effective features to train the ANN model. The final ANN model had 15 neurons in the input layer including selected image features, 11 neurons in the hidden layer, and one neuron in the output layer including the weight of the cows. The precisions of the artificial neural network in the training, validation, and test phase were 0.974, 0.970, and 0.981, respectively. The results showed that the final ANN model had acceptable precision in all light, medium, and heavy-weight cows, and the size and the age of animals did not have a significant effect on the precision of the artificial neural network model. A correlation between the actual weight of Sistani cows and the weights predicted by the ANN model was 98.3%. The average error of the model in predicting the weight of cows was 1.11%. In the practical test, a 2.32 kg deviation was observed between the predictions of the ANN model and actual weights in Sistani cows. The accuracy of the ANN model for predicting the weight of Sistani cows in the present study is acceptable and within the range of the other reports. The proposed method based on image processing and ANN, had acceptable results in predicting the weight of Sistani cows. Given the difficulties of weighing Sistani cows as heavy livestock and sometimes the time-consuming process, it seems that the use of new technologies such as computer vision methods can be a good alternative to conventional weighing methods and facilitate and reduce recording costs of Sistani cows.

Reducing the population size of Sistani cattle due to the multi various reasons during the last years, was the motivation of the current study. Therefore, the aim of this study was to investigate the factors of population decline and strategies to increase the population and revive this breed. For this purpose, a questionnaire was developed using the Delphi method. Questionnaire was included 21 reasons and 18 strategies related to the situation of Sistani cattle population. Based on the breeders’ insight, drought in the Sistan region during last three decades was considered as the most important factor in reducing the population of Sistani cattle, and for both groups, the least important factor affecting population decline is the uneconomic importance of Sistani cattle (P < 0.05). Both groups were agreed that climatic factors are the most important in reducing the population of Sistani cattle and almost no differences in this regard were observed between the two groups (P > 0.05). From the breeders' point of view, economic support for Sistani cattle breeders is the most important way to support the Sistani cattle population, while experts believe that the most important way is to rehabilitate Hamoun Wetland. In general, the results of this study showed that due to the changing and varying climate of Sistan region, attention to economic issues and support for breeders can largely compensate for the problems caused by unfavorable climatic conditions in the rehabilitation of Sistani cattle population.

This research was conducted to investigate the possibility of using visual machine technology in measuring the body dimensions of Sistani cows. For this purpose, the record of body dimensions including length, shoulder height, hip height and chest circumference of 179 heads of livestock in Zahak Sistani cow breeding station, were measured at different time points, using a tape meter. At recording time, digital images were taken using the canon camera from the lateral view of cattle from distance of 2 meters. Digital image processing and feature extraction were performed using Graphical Unit Interference of MATLAB software. The feature of digital images as input and different body dimensions of cows as output of Artificial Neural Network(ANN) were used in the training and modeling phase. The results showed that, out of 22 features extracted from the images of Sistani cows, the 15 effective features, such as Equivalent Diameter, Major Axis Length, Minor Axis Length, Bounding Box, Convex Area, Filled Area, Area, Perimeter, and the number of white pixels of image (NNZ) had a significant correlation with the body dimensions of the Sistani cow(p<0.01). Body dimensions of Sistani cows including body length, shoulder height, hip height, and chest girths were estimated with accuracy of 0.98, 0.97, 0.97and 0.98%, by the ANNs model, respectively. The results of the present research showed that Artificial Intelligence Technology can act as a suitable alternative to biometric evaluation of Sistani cows and save time and relevant costs.

It has been shown that the occurrence of any changes in the function of DNMTs genes has a significant effect on both the embryo development and birth weight in mammals; therefore, the aim of the present study was to investigate the presence of DNA polymorphisms in the members of DNA methyl transferase superfamily and its relationship with birth weight in Sistani cattle’s. Blood sampling was done randomly from 60 Sistani cows. DNA extraction was performed using phenol chloroform method. Candidate region for the presence of functional polymorphism within the DNMTs gene including the 114-bp fragment of the exon 33 of the DNMT1 gene, the 176-bp fragment of the intron 4 of the DNMT3a gene, and the 207-bp fragment in intron 3 of the DNMT3b gene were amplified by polymerase chain reaction. PCR-SSCP followed by polyacrylamide gels silver stain analysis was done to evaluate the presence of candidate mutations in target gens. The result analysis of all analyzed region did not show any mutations in the investigated DNMTs genes. Based on the results of this study, it seems that the lack of observation of genetic diversity in the studied regions could be related to the evolutionary selection against occurrence of specific mutations in the analyzed population. In general, based on the results of this study, analysis of genetic diversity in the studied genomic region may not be effective in identifying effective markers for breeding programs in Sistani cows.

To determine of MEG9 gene polymorphism and its association with birth weight in Sistani cattle, the blood sample of 130 calves (male and female) were randomly collected belonging to Sistani cattle Research Center. The DNA extraction from whole blood was performed using phenol-chloroform procedure. Polymerase chain reaction was performed for amplification of 210-bp segments of exon 3 of MEG9 gene using specific primers. Detection of mutations were carried out by electrophoresis of poly-acrylamide gel 8% and silver staining using SSCP method. Association study of allele patterns with birth weight was done with fixed linear model and comparison of means was conducted by Tukey-kramer method. Results showed that three allele patterns including q1, q2 and q3 in PCR product of MEG9 gene in Sistani cattle with frequency of 37, 34 and 29 % respectively. The genetic diversity in MEG9 gene had significant correlation with birth weight (P

The objective of the present study was to determine the extent of BOLA-DRB3 locus polymorphism in a herd of Sistani Cattle and their associations with body weight traits including birth weight, 3, 6, 9 and 12 months, and blood serum biochemical such as total protein, albumin and globulin. For this, blood samples collected randomly from 143 Sistani Cattle in a research herd at the Sistani Cattle Research Centre. DNA extraction from complete blood samples was performed with optimized saline method. The amplification of the 284-bp fragment of the BoLA-DRB3 gene was performed in two steps. In the first step, the HLO30 and HLO31 primers were used and in the second step the HLO30 and HLO32 primers were used. Then, the amplified fragment was digested by HaeIII enzyme. The digestion products were resolved on electrophoresis of 3% gel agarose. Population and genetic structure was calculated with POPGENE software and probable genotype patterns association with body weight traits and blood biochemistry traits were implemented by fixed linear model. The results of this study revealed two alleles and three genotypes as aa, bb and ab in locus of BOLA-DRB3.2 in Sistani cattle. Genotype frequencies of aa, bb and ab were 30, 41 and 28%, respectively, and alleles frequencies of a and b were calculated 44 and 56%, respectively. In this experiment, the Shannon index (I), Nei's index, heterozygosity was observed and the expected heterozygosity were calculated 69, 49, 28 and 50 respectively. The analysis of likelihood ratio test and X2 in BOLA-DRB3.2 showed that the population of Sistani cattle studied in this study was beyond the Hardy-Weinberg equilibrium. Also, the correlation of different genotypes with body weight traits including birth, 3, 6, 9 and 12 months, and biochemical characteristics of serum were not significant.

The aim of this study was to design the base breeding plan in Sistani cattle by using of stochastic simulation. For this purpose, three population size (500, 1000, 2000 heads), three levels of herd number (5, 10, 20), two levels of selection intensity (high and low) and two levels of mating method (random and Minimum co-ancestry) were combined together and in total 36 scenarios were compared for 30 years. Results showed that the effect of all factors on the annual genetic gain for total merit, the annual genetic gain of birth weight (except in mating method and selection intensity) and yearling weight, the annual inbreeding rate (except in herd number), generation interval (except in mating method and selection intensity) and selection accuracy of sire and dames was significant. The annual genetic gain in high selection intensity, population size of 2000 and less number of herd (5 herds) was 8.5, 93.2 and 38 percent higher than low selection intensity, population size of 500 and more number of herd (20 herds). Despite having less total genetic gain (11 percent) in Minimum co-ancestry mating, the annual inbreeding rate in this mating method was 18 percent lower than random mating. The results of this study showed that less number of herd, large population size and high selection intensity considered in breeding plans of Sistani cattle and minimum co-ancestry mating used for control of population inbreeding.