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This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Agricultural science --- Life sciences: general issues --- Botany & plant sciences --- Animal reproduction --- Probability & statistics --- open access --- Statistical learning --- Bayesian regression --- Deep learning --- Non linear regression --- Plant breeding --- Crop management --- multi-trait multi-environments models

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In this book, the subject of design and analysis of experiments has been covered in simple language by giving basic concepts of various designs and essential data analysis steps of designed experiments. It has become clear that among researchers, mainly from the areas of food and agricultural sciences, there is a great need for a reference work on design and analysis of experiments that covers basic concepts, provides examples of varied situations that require the use of the experimental designs and that offers clear steps required for the correct analysis execution. This book covers such needs while also sharing codes in the Statistical Analysis Systems (SAS) for each of the designs covered using Proc Glimmix to perform the analysis. It is hoped that this will allow readers to directly analyze the data from their experiments.

Experimental design. --- Agriculture. --- Food science. --- Design of Experiments. --- Food Science.

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In this book, the subject of design and analysis of experiments has been covered in simple language by giving basic concepts of various designs and essential data analysis steps of designed experiments. It has become clear that among researchers, mainly from the areas of food and agricultural sciences, there is a great need for a reference work on design and analysis of experiments that covers basic concepts, provides examples of varied situations that require the use of the experimental designs and that offers clear steps required for the correct analysis execution. This book covers such needs while also sharing codes in the Statistical Analysis Systems (SAS) for each of the designs covered using Proc Glimmix to perform the analysis. It is hoped that this will allow readers to directly analyze the data from their experiments.

Experimental design. --- Agriculture. --- Food science. --- Design of Experiments. --- Agriculture. --- Food Science.