Linear and generalized linear models are powerful statistical tools for agronomic researches, which have been widely used in the data analysis of field trails and breeding studies. The aim of this course is to review and extend student's knowledge of linear and generalized linear models. It provides a solid basis for further studying other subjects of agronomy. Focus will be placed on introducing the fundamental theory and general techniques for parameter estimation and inference. Some related issues in agricultural studies, for example, the estimability of contrasts and testability of hypotheses in comparative experiments; statistical inference on heritability and other variance components in breeding studies, will be discussed and illustrated by practical examples. To consider different experimental scenarios, some classes of generalized linear models, including the logistic and Poisson regression models, will be introduced for characterizing qualitative response variables. In addition, students will also learn how to use R to analyze real-life data during lecture.