In this paper, six univariate and two multivariate best linear unbiased prediction (BLUP) models were tested for the estimation of breeding values (BV) in Holstein Friesian cattle in Serbia. Two univariate models were formed using the numerator relationship matrix (NRM), four using the genomic relationship matrix (GRM). Multivariate models contained only an NRM. Two cases were studied, the first when only first lactations were observed, and the second when all lactations were observed using a repeatability model. A total of 6041 animals were included, and of them, 2565 had data on milk yield (MY), milk fat yield (FY), milk fat content (FC), milk protein yield (PY) and milk protein content (PC). Finally, out of those 2565 cows, 1491 were genotyped. A higher accuracy of BV was obtained when using a combination of NRM and GRM compared to NRM alone in univariate analysis, while multivariate analysis with repeated measures gave the highest accuracy with all 6041 animals. When only genotyped animals were observed, the highest accuracy of the estimated BV was calculated by the ssGBLUPp model, and the lowest by the univariate BLUP model. In conclusion, the current breeding programs in Serbia should be changed to use multivariate analysis with repeated measurements until the optimal size of the reference population, which must include genotyping data on both bulls and cows, is reached.

The aim of this research was to determine the influence of lactation number and parity on milk yield of Saanen goat’s breed. It has been concluded that milk yield was higher for goats that had given birth to twins and three kids than for goats that had given birth to a single kid (P<0.05). The longevity has a great positive impact on production, given that goats in the fifth lactation produced more milk than goats in first or second lactation (P<0.05), while goats in the sixth lactation still produced more milk than goats in first lactation (P<0.05). During the period from 2014- 2019, the research was conducted on a farm of multiparous Saanen goats in Vojvodina (northern part of Serbia). In the observed period, the farm had between 350 and 400 goats per milking, and the research included a total of 1,628 concluded lactations, within which the first lactations were the most 484, then the second 381. According to the number of lactations, goats were grouped into classes from one to seven and from one to three for parity. By comparing each individual lactation, it was determined that in first lactation goats produced significantly lower milk than goats in the rest lactations(P<0.05), except the seventh. Research has shown that lactation number has a significant influence on milk yield (P=0.00). Increasing the fertility of goats significantly affects the production capacity of dairy goats, since does with a larger number of kids in the litter had a significantly higher milk production (P<0.05). Due to the larger number of kids in the litter, milk production also increases. Likewise, it is assumed that the increase in milk yield of goats with two and three kids is a consequence of a larger placenta, i.e. stronger lactogenic activity during pregnancy.

To maximize protein production from insects, it is essential to understand their nutritional requirements, production characteristics, and the chemical properties of the resulting feed, along with how these factors depend on the insects’ diets. This research involved the use of nine different insect diets, all based on wheat bran. Corn and soybean meal were included to enhance the energy and protein content. The weight gain of larvae was significantly affected by the level of protein in their feed; however, the energy level did not have a significant impact on the weight gain measured in grams per day per tray. This gain ranged from 0.57 g/day per tray for insects fed low-energy, low-protein diets to 0.71 g/day per tray for those fed high-protein, medium-energy diets. Feed conversion ranged from 1.83 to 2.34 and was significantly influenced by energy and protein levels. The chemical composition of reared insects showed significant differences, particularly in fat content. The fat content ranged from 38.5% with a low-protein diet to just 26.9% with a high-protein diet. In contrast to fat content, the variability in protein content was smaller, ranging from 44.8% in a low-protein, high-energy diet to 51.9% in a high-protein, high-energy diet.

Effects of a novel carbohydrate fraction on broiler performance and intestinal function

Summary: This paper presents a deep neural network (DNN) approach designed to estimate the milk yield of Holstein-Friesian cattle. The DNN comprised stacked dense (fully connected) layers, each hidden layer followed by a dropout layer. Various configurations of the DNN were tested, incorporating 2 and 3 hidden layers containing 8 to 54 neurons. The experiment involved testing the DNN with different activation functions such as the sigmoid, tanh, and rectified linear unit (ReLU). The dropout rates ranging from 0 to 0.3 were employed, with the output layer using a linear activation function. The DNN models were trained using the Adam, SGD, and RMSprop optimizers, with the root mean square error serving as the loss metric. The training dataset comprised information from a unique database containing records of dairy cows in the Republic of Serbia, totaling 3,406 cows. The input parameters (a total of 27) for the DNN included breeding and milk yield data from the cow’s mother, as well as the father’s ID, whereas the output parameters (a total of 8) consisted of milk yield parameters (a total of 3) and breeding parameters of the cow (a total of 5). Training iterations were conducted using a batch size of 8 over 500, 1000, and 5000 epochs.

Summary: Wheat grain is commonly used for animal feeding in some regions while it is rarely used in others. Its nutrient composition is quite similar to more commonly used corn grain, but there are some concerns regarding its effect on animal performance and health. However, it appears that usage of wheat grain in animal feeding is dominantly affected by economic rather than nutritional reasons. When used with caution, wheat can be a good alternative to corn. In order to test this, 20 Wurttemberg lambs, three months old, were randomly separated in two groups and fed diets containing 600 g of corn grain, or 300 g of corn grain and 300 g of wheat grain, for a period of one month. The used grains were whole, non processed grains. Two diets contained the same commercial protein supplement and alfalfa hay was offered ad libitum. Both groups accepted their experimental diet well and readily consumed the amounts offered. No health problems were observed. Growth rate was higher in male than in female lambs, but was not affected by wheat inclusion.