a. aghashahi

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.

Fodder beet is a valuable source of fodder in livestock diets due to favorable agronomic characteristics such as resistance to salinity and drought, less water requirement, and proper nutritional characteristics such as forage production and silage with high nutritional value, good taste, and good resistance to environmental changes. Despite the long history of using fodder beet in animal rations, its nutritional issues and physicochemical properties of it are not fully understood and further research is needed. Silage of fodder beet can be used well in the diet of all ruminants, but studying the proper ways of silage and controlling the physical and chemical properties of silage is one of the important issues that should be addressed. In addition to the stated benefits for fodder beet, such as low dry matter content, fiber, and fat, disruption of ion exchange and change in silo buffering capacity, as well as the difficulty of processing it for permanent consumption of livestock and also important restrictions such as high sugar and moisture in the roots and excess leachate and lack of production of quality silage are the most important obstacles to the consumption of this silage. Harvest time of fodder beet coincides with the heat of the summer season and the occurrence of numerous problems, so it is necessary to silo it to prevent spoilage with the correct method. Today with the introduction of new improved cultivars and the growth of processing technology, these limitations have been almost removed. This study aimed to evaluate some of the physicochemical parameters of ensiled fodder beet with and without Lactobacillus buchneri as a microbial additive.

Some physicochemical parameters of ensiled fodder beet with and without Lactobacillus buchneri as a microbial additive in combination with 10 and 15% dry beet pulp were investigated through a 3×2 factorial arrangement in four replicates. Different silages included: 1) 100% pure fodder beet without additive (control), 2) control + 10% dry beet pulp, and 3) control + 15% dry beet pulp. The microbial additive was added at the level of 6×1010 CFU/g of dry matter. 24 plastic bags containing 25 kg of different material were compacted, sealed, and stored at room temperature. After 75 days, four bags of each group were opened and pH, temperature, and quality were immediately determined. Also, a sample from each bag was used to measure the chemical compositions. Measurements of temperature and pH were performed. Two samples of silage, one for extraction and determination of ammonia nitrogen (using selective ion absorption electrode) by AFIA recommendations and the other sample for measurement of dry matter, crude protein, and ash according to the AOAC method were used. Measurement of NDF and ADF were done according to the Van Soest method. To evaluate the aerobic stability of silages when opening plastic bags, the characteristics of color, odor, and texture of silage in the range of 1 to 10 for high to poor quality were determined using the modified Konigsberg Scores method. Data were analyzed using a completely randomized design in a 3×2 factorial arrangement in four replicates using the mixed model procedure of the SAS program.

Experimental groups with 10 and 15% of dry beet pulp with microbial additive had a better quality (score: 6.5±0.16 vs. 9.4±0.21; pH: 4.04±0.06 vs. 4.59±0.08) than the control group (P<0.01). The type of ensiled materials had a significant effect on chemical compositions (DM, CP, NDF, and ADF) and the aerobic stability of silages at opening time (P<0.01). But microbial additives almost had no significant effect on the traits. Watery loss of silage for the control group was higher and its quality was lower than that in other groups (P<0.01). The best quality and more stability of silage were observed in the group with fodder beet+15% of dry beet pulp which is advisable.

According to the results of this study, adding 10 and 15% of dry beet pulp to fodder beet silage improved the appearance and aerobic stability of the resulting silages compared to the control group. Evaluation of appearance traits and pH of the samples showed that the treatments with dry pulp and the microbial additive had better quality than the control group. The type of mixture used had a significant effect on the chemical composition of silage and its aerobic stability when opened, but the microbial additive did not show a significant effect in most cases. The watery loss in the control group was higher than the other treatments and had a worse appearance quality than other silages. Overall, the most suitable silage composition in this experiment was a mixture of the control group+15% sugar beet pulp, which had better appearance quality, longer shelf life, higher dry matter level, and lower ammonia nitrogen than the other two groups.

In this research, an ozone gas-based oxidation technique for enhancement of delignification along with urea-based chemical treatment as a nitrogen source was used to improve the nutritional value of wheat straw. The experiments were performed with four treatments and three replications in a completely randomized design. Experimental treatments of wheat straw were 1. Untreated sample, 2. Separate treatment with urea (3% w/w, 24 h), 3. Separate treatment with ozone (3 g/h, 45 min), and 4. Combined treatment of ozone (3 g/h, 45 min) with urea (3% w/w, 24 h). The results showed that ozonized treatments had a significant improvement (P<0.01) in the nutritional value of wheat straw compared to the untreated and urea-treated samples. Also, According to the results, the combined ozone-urea treatment increased the nutritional value of wheat straw more than urea and ozone treatment alone. The combined ozone-urea treatment caused a reduction of 18.28%, 7.97%, and 50% in NDF, ADF, and ADL, respectively. Also, improving the nutritional value was observed by increasing 333.3%, 22.5%, 23.67%, and 35.34% in CP, bonded organic-N, TDN, and RFV, respectively, and improvement of gas production parameters were obtained by increasing 100.21%, 63.76%, 34.93%, and 46.43% for GP24, ME, IVOMD24, and SCFA, respectively, as compared to control sample. Overall, from the results of this study, it can be concluded that combined ozone-urea treatment is comparable to traditional and existing novel treatment methods, which due to its ability to perform at ambient temperature, atmospheric pressure, and short-time treatment, can be used as a suitable alternative in agricultural straw treatments.