Big Data approaches as a support for precision livestock farming techniques

With the advent of new technologies it is increasingly easier to find data of different nature from even more accurate sensors that measure the most disparate physical quantities and with different methodologies. The collection of data thus becomes progressively important and takes the form of archiving, cataloging and online and offline consultation of information. Over time, the amount of data collected can become so relevant that it contains information that cannot be easily explored manually or with basic statistical techniques. The use of Big Data therefore becomes the object of more advanced investigation techniques, such as Machine Learning and Deep Learning. In this work some applications in the world of precision zootechnics and heat stress accused by dairy cows are described. Experimental Italian and German stables were involved for the training and testing of the Random Forest algorithm, obtaining a prediction of milk production depending on the microclimatic conditions of the previous days with satisfactory accuracy. Furthermore, in order to identify an objective method for identifying production drops, compared to the Wood model, typically used as an analytical model of the lactation curve, a Robust Statistics technique was used. Its application on some sample lactations and the results obtained allow us to be confident about the use of this method in the future.

Applied genomics in livestock: genome-wide association studies and population genomics analysis in pig and rabbit breeds

The domestication and selection processes in pigs and rabbits have resulted in the constitution of multiple breeds with broad phenotypic diversity. Population genomics analysis and Genome-wide association study analysis can be utilized to gain insights into the ancestral origins, genetic diversity, and the presence of lethal mutations across these diverse breeds. In this thesis, we analysed the dataset obtained from three Italian Pig breeds to detect deleterious alleles. We screened the dataset for genetic markers showing homozygous deficiency using two approaches single marker and haplotype-based approach. Moreover, Genome-wide association study analyses were performed to detect genetic markers associated with pigs' reproductive traits. In rabbits, we investigated the application of SNP bead chip for detection signatures of selection in rabbits using different methods. This analysis was implemented for the first time in different fancy and meet rabbit breeds. Multiple approaches were utilized for the detection of the selection of signatures including Fst analysis, ROH analysis, PCAdapt analysis, and haplotype-based analysis. The analysis in pigs was able to identify five putative deleterious SNPs and nine putative deleterious haplotypes in the analysed Italian Pig breeds. The genomic regions of the detected putative deleterious genomic markers harboring loss of function variants such as the Frameshift variant, start lost, and splice donor variant. Those variants are close to important candidate genes such as IGF2BP1, ADGRL4, and HGF. In rabbits, multiple genomic regions were detected to be under selection of signature. These genomic regions harbor candidate genes associated with coat color phenotype (MC1R, TYR, and ASIP), hair structure (LIPH), and body size (HMGA2 and COL2A1). The described results in rabbits and pigs could be used to improve breeding programs by excluding the deleterious genetic markers carriers and incorporating candidate genes for coat color, body size, and meat production in rabbit breeding programs to enhance desired traits