Introduction to Experimental Designs With Proc Glimmix of SAS: Applications in Food Science and Agricultural Science

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.

Josafhat Salinas Ruiz holds BS in Agro-industrial Engineering from Universidad Autónoma Chapingo, Mexico, Masters in Statistics from Colegio de Postgraduados of México and PhD in Biometry from the University of Nebraska-Lincoln, USA. Josafhat Salinas-Ruíz is currently a Professor of Statistics, Multivariate statistics, and Experimental Designs at Colegio de Postgraduados campus Córdoba, Mexico. His areas of interest include advanced statistical modeling in plant sciences, agriculture, and agronomy, generalized linear mixed models, multivariate analysis and experimental designs.

Osval Antonio Montesinos López holds a BS in Agro-industrial Engineering from Universidad Autónoma Chapingo of México, Masters in Statistics from Colegio de Postgraduados of México and PhD in Statistics and Biometry from the University of Nebraska-Lincoln. Osval A. Montesinos-López is currently a Professor of Statistics, Probability and Statistical Learning at the University of Colima, México. His areas of interest include the development of novel genomic prediction models for plant breeding, high-dimensional data analysis, generalized linear mixed models and Bayesian analysis, multivariate analysis, and experimental designs. He has contributed univariate and multivariate genomic prediction models for predicting breeding values in plants with normal, binary, count and ordinal phenotypes. He also has taught courses on genomic prediction, statistical and machine learning in Mexico, the United States of America, Brazil, Peru, Nigeria, France and India.

José Crossa holds a BS in Agriculture from Republic University of Uruguay and a PhD in Statistics and Quantitative Genetics from the University of Nebraska-Lincoln. He has helped define key methodologies for conserving and using the center's maize genetic resources, covering proper regeneration procedures and strategies for forming core subsets of large germplasm collections. Crossa's became Head of the Biometrics and Statistics Unit of CIMMYT and developed theoretical and practical work on genetic resources conservation that made him to be selected the best scientist of the CGIAR Centers in 2008. His substantive body of research and publications has addressed many other areas of breeding and agronomy research, including developing new statistical models for genotype x environment, and QTL x environment interactions, general breeding and experimental design, hybrids and heterotic patterns, and association mapping, to name a few important subjects, and enjoys international acclaim and application. Crossa was given the Distinguish Scientist recognition in CIMMYT and is a Fellow of the Agronomy Society of America and of the Crop Science Society of America, Member of the Mexican Academy of Science, Member of the National Research System of the National Council of Research and Technology (CONACYT) of Mexico, invited professor at Universities in Mexico and Uruguay, and Adjunct Professor at the University of Nebraska. Recently, Crossa and colleges impacted plant breeding by being one of the first researchers in showing genomic-enabled predictions models with high accuracy using pedigree and markers information applied in massive maize and wheat field data.