Selection Index And Introduction To Mixed Model Methods

Parameters, statistics, and expected values; A little about matrix algebra; Quantifying the simple mendelian model; A short summation on population genetics; Genes identical by descent-the basis of genetic likeness. Genetic values and genetic covariances; The selection index; Determining the coeficients for selection index equations; Sire evaluation, example of application of selection index; Probability statements about true value. Superiority of selected groups; Selection index flow chart for single traits; Selection with more than one trait measured; Using records on all traits of relatives; Selection index for categorical data. Selection for embedded traits: maternal effects; Selection when traits influenced by grandmaternal and maternal effects;Fetal effects model(sire of fetus effect).

Dairy Science, Four Volume Set includes the study of milk and milk-derived food products, examining the biological, chemical, physical, and microbiological aspects of milk itself as well as the technological (processing) aspects of the transformation of milk into its various consumer products, including beverages, fermented products, concentrated and dried products, butter and ice cream. This new edition includes information on the possible impact of genetic modification of dairy animals, safety concerns of raw milk and raw milk products, peptides in milk, dairy-based allergies, packaging and shelf-life and other topics of importance and interest to those in dairy research and industry. Fully reviewed, revised and updated with the latest developments in Dairy Science Full color inserts in each volume illustrate key concepts Extended index for easily locating information

Innumerable publications on livestock production are available in the world market. The book under discussion has not been produced to burden the market with another such publication rather it has been brought out employing a novice format to meet the requirements of students, researchers who are working in different parts of the world in different environments.

This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.

An overview of crop improvement; Analysis of genotype by environment interactions; Interpretation of genotype by environment interactions; Integrated approaches to plant improvement; Synthesis of strategies for crop improvement.

"Throughout the last century, specialisation and intensification were buzz words for farmers in the Western world. However, this approach has not resulted in sustainable development as evidenced by the fact that scientists now need to create technologies to reduce negative impacts. In this book we demonstrate that an alternative exists. Case studies from Bangladesh, Thailand, and Vietnam show that integration and diversification increase both farm productivity and farmers’ incomes. By adopting a participatory approach, farmers and scientists identified a range of technologies that strengthen the positive impacts of integrated aquaculture-agriculture systems for the environment. This book is a collection of refereed papers on a controversial subject in agricultural development. Arguing that sustainability of fish culture in ponds needs a new paradigm - feed the pond to grow fish - two chapters focus on nutrient cycling in such systems. Another chapter makes the case for breeding Nile tilapia for resource poor farmers and presents practical options to avoid the pitfalls that arise from natural tilapia mating in low-input ponds. The book contains chapters on livelihood and development aspects and ends with a general discussion completing the picture of the integrated aquaculture-agriculture systems. Overall it composes a review which addresses one of the key issues of the new century: how to sustainably produce food without compromising environmental integrity."

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

This newly updated and revised volume of the Encyclopedia of Sustainability Science and Technology (ESST) details the role of Animal Breeding and Genetics in the sustainability of animal agriculture. The volume covers scientific principles and applications includes the current science used to advance cattle, poultry, swine,sheep, and equine populations, as well as the future role of techniques such as gene editing. International leaders in the field explain foundational concepts such as heritability, the covariance between relatives, statistical approaches to predicting the genetic merit of individuals, and the development and advancement of molecular techniques to elucidate changes in the DNA sequence that underly phenotypic variation. The use of genetic-based tools to improve animal agriculture and meet consumer demands across species is treated in detail. Readers will gain an understanding of how global livestock producers have implemented advanced genetic selection tools and used them to improve reproduction, production, efficiency, health, and sustainability. The interactions of genetics and production environments, and the genetic components of the complex interactions among animals are also discussed. The future of Animal Breeding and Genetics, including the challenges and opportunities that exist in feeding a growing world population, are addressed.