Mixed Models in R for Agriculture Researchers

Mixed Models in R: A Practical Guide for Agriculture Researchers is a clear, hands-on book that helps agriculture and biological researchers analyze trial data with confidence using mixed models in R. It explains the difference between fixed and random effects in simple language, walks you through long vs. wide data formats, and shows how to handle common designs like split-plot, strip-plot, and repeated-measures experiments. The book is built around practical examples, reusable R code, diagnostics, and publication-ready output, making it especially useful for students, researchers, and professionals working with real field and livestock data.

If you work with agricultural trials, livestock studies, or repeated-measures data, this book gives you a practical path from raw data to correct analysis in R. Instead of overwhelming theory, it focuses on the exact steps you need to set up your data, fit the right mixed model, check assumptions, and present results clearly.

The book covers the most common problems researchers face, including non-independent observations, nested and crossed random effects, split-plot error structures, model diagnostics, and mean comparisons with emmeans. It also includes a reusable script template so readers can apply the same workflow to future datasets with less guesswork.