Agustin Blasco – författare

Breeding for improved results is not a new phenomenon, but with current issues such as climate change, food insecurity and the need for greater sustainability within the agriculture industry, breeding and genetic improvement is a fundamental aspect of animal science. Bringing together the three main areas of animal breeding into one text, this book combines quantitative genetics and the methodology for animal genetic improvement, specific aspects of livestock species genetics, and a review of how genetic improvement companies are organized and their selection programs. Written with a student focus for those studying animal agriculture and animal science, it covers the quantitative genetics needed to apply genetic improvement methods, selection indices, use of the linear model in genetic improvement, and genomic selection and gene detection. It also expands into breed and conservation programs, genetic improvement schemes in both prolific and low-prolific species, and the future of animal genetic improvement. This book: - Aids learning, organized with each chapter benefiting from an introduction to the main topics to be learnt, and ending with a list of important main concepts, problems to be solved (with their solutions) and a test for self-assessment. - Supplies further mathematical demonstrations in handy extra appendices. - Uses examples from across pigs, poultry, rabbits, cattle, sheep, goats to provide a rounded overview of the topic. Beyond knowing how to apply a program in R or Python, it is essential that students understand the fundamental concepts of quantitative genetics. Providing an essential skill for their training, this book also aids professional development, and ensures sound interpretation of the results of selection programs.

In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.

In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.