The prediction that the world population will reach almost 10 billion people by 2050 means an increase in pork production is required. Efforts to meet increased demand have made pig production one of the most technologically advanced branches of production and one which is growing continuously. Precision Livestock Production (PLF) is an increasingly widespread model in pig farming and describes a management system based on the continuous automatic monitoring and control of production, reproduction, animal health and welfare in real time, as well as the impact of animal husbandry on the environment. Today, a wide range of technologies is available, such as 2D and 3D cameras to assess body weight, behavior and activity, thermal imaging cameras to monitor body temperatures and determine estrus, microphones to monitor vocalizations, various measuring cells to monitor food intake, body weight and weight gain, and many others. By combining and applying the available technologies, it is possible to obtain a variety of data that allow livestock farmers to automatically monitor animals and improve pig health and welfare as well as environmental sustainability. Nevertheless, PLF systems need further research to improve the technologies and create cheap and affordable but accurate models to ensure progress in pig production.

Autochthonous pig breeds provide products of differentiated quality, among which quality control is difficult to perform and insufficient for current market requirements. The present research evaluates the predictive ability of near-infrared (NIR) spectroscopy, combined with chemometric methods as a rapid and affordable tool to assure traceability and quality control. Thus, NIR technology was assessed for intact and minced muscle Longissimus thoracis et lumborum samples collected from 12 European autochthonous pig breeds for the quantification of lipid content and fatty acid composition. Different tests were performed using different numbers of samples for calibration and validation. The best predictive ability was found using minced presentation and set with 80% of the samples for the calibration and the remaining 20% for the external validation test for the following traits: lipid content and saturated and polyunsaturated fatty acids, which attained both the highest determination coefficients (0.89, 0.61, and 0.65, respectively) and the lowest root mean square errors in external validation (0.62, 1.82, and 1.36, respectively). Lower predictive ability was observed for intact muscles. These results could contribute to improve the management of autochthonous breeds and to ensure quality of their products by traditional meat industry chains.

Anthropogenic activities and rapidly increasing climate change have led to a significant loss of genetic diversity in domestic animals. Genealogical data have traditionally been used to monitor genetic diversity. However, due to dependency on pedigree completeness and significant errors that can occur in genealogical records, inaccurate estimation of population parameters, such as the inbreeding coefficient or effective population size, might occur. To reduce possible errors, it is necessary to combine genealogical data with molecular data. An integrated approach using genealogical and molecular data leads to the optimization of breeding programs while controlling the inbreeding that can occur within the population of domestic animals. Molecular techniques such as single nucleotide polymorphism (SNP) genotyping, whole-genome sequencing (WGS), or genome-wide association studies (GWASs) enable a detailed understanding of breed-specific genetic profiles and their use in conservation programs. In addition, molecular methods such as structural variation (SV) analysis and pangenome and epigenomic profiling provide a more comprehensive insight into genetic diversity. The conservation of genetic diversity is of particular importance for an autochthonous domestic breed due to its resilience to unfavorable climatic conditions, its specific productive traits, and its disease resistance. A combined approach of genealogical and molecular data helps to maintain genetic diversity and sustainable agricultural systems under evolving environmental challenges.

The aim of this study was to investigate the influence of genotype (GT) and seasonality (NP) on the quality parameters of sow colostrum and evaluate the efficiency of the radial immunodiffusion (RID) analysis and the Brix refractometer in determining the IgG concentration. This study was conducted on 240 sows that originated from two genotypes, namely GT1 (TOPIGS, n = 120) and GT2 (Pig Improvement Company, n = 120), during the three farrowing periods: the winter farrowing period (WNP, n = 80), the summer farrowing period (SMP, n = 80) and the spring farrowing period (SSP, n = 80). The significant interaction effect was observed for protein (p < 0.0001), lactose (p < 0.05) and non-fat solids (SNT) (p < 0.001). At the same time, the interaction effect influenced the IgG concentration measured with the Brix refractometer (p < 0.0001) and RID (p < 0.0001). Pearson’s correlation coefficient showed that Brix percentage was positively correlated with RID results (r = 0.52, p < 0.0001), while the Bland–Altman plots indicated a mean bias of −1.93. Partial eta-squared analysis (η²) showed that the genotype explained the largest proportion of variance in fat content (η² = 0.136) and IgG concentration (η² = 0.164), while interaction effects were largest for protein (η² = 0.072). The results of this study show that genotype and seasonality influence sow colostrum quality, which indicates the importance of genotype−seasonality interactions in breeding programs for optimizing the colostrum quality and piglet survival.

This study investigated the biochemical and hematological parameters (at the beginning, after 90 days, and at the end of the experiment) of the pure genotype Black Slavonian (BS) pigs and crossbreeds of Black Slavonian and Duroc (BSxD) pigs, f...

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, the scientific journal of the Mendel University in Brno, Czech Republic